How Can Odors Be Measured? An Overview of Methods and Their Applications

Electronic nose for odor monitoring at a landfill fenceline: Training and validation of a model for real-time odor concentration measurement

In recent years, electronic noses, or more generally Instrumental Odor Monitoring Systems (IOMS), have aroused increasing interest in the field of environmental monitoring. One of the most interesting applications of these instruments is the real-time estimation of the odor concentration at plant fencelines to continuously monitor odor emissions and identify anomalous conditions. In this type of application, it is possible to setting a "warning" threshold, enabling the continuous check of proper functioning of the plant and sudden intervention in case of malfunctions, preventing, at the same time, the risk of odor events at the receptors. For this purpose, it is necessary to provide a continuous, fast and reliable measurement of the odor concentration, which is nowadays one of the main challenges of this technology. In this context, this work proposes the development of a quantification model for quantifying odors detected at the fenceline of a landfill characterized by very different odor fingerprints. A double-step quantification model, firstly identifying the different odor classes to which the ambient air monitored at the fenceline by the IOMS belong to, and then developing different specific PLS regression models for each of the odor classes identified, was developed. The results of the proposed quantification model were compared to the ones obtained developing a "global" quantification model, which implements the regression on the globality of the training set, without differentiating between the odor classes. Then, they were further evaluated by comparison with the odor events detected at the sensitive receptor by another electronic nose. Moreover, the combined evaluation of the odor events at the plant fenceline and the receptor, respectively, together with the meteorological data highlighted the need of identifying variable warning thresholds for the odor concentrations at the fenceline according to effectively account for meteorological conditions and produce an output that is more correlated with the probability that an odor is perceived outside of the 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.

Superior high-efficiency and high-throughput volatile flavor extraction of Japanese fermented seasonings by solvent-assisted stir bar solid extraction with reverse extraction

Fermented seasonings have pleasant flavors that stimulate our appetite. Their flavoring properties change depending on factors such as their materials and fermented conditions. Therefore, a comparative analysis of their flavor is important when evaluating their quality. However, seasonings contain high levels of various matrices such as sugars, proteins, lipids, and ethanol, making it difficult to extract aroma compounds efficiently from them. In this study, we verified a high-efficient and high-throughput volatile flavor analysis of fermented seasonings by solvent-assisted stir bar solid extraction (SA-SBSE) with reverse extraction. We applied SA-SBSE to Japanese fermented seasonings, soy sauce, miso (fermented beans), and mirin (sweet rice wine) and compared their profiles with those from other common extraction methods, headspace gas-solid-phase microextraction (HS-SPME), liquid extraction with solvent-assisted flavor evaporation (LE-SAFE), and conventional SBSE (C-SBSE). The aroma properties and profiles of extracts from SA-SBSE were close to those of the original sample, being similar to that of LE-SAFE. In addition, potent aroma compounds in each sample were extracted by SA-SBSE and LE-SAFE, which were far superior to those by C-SBSE. For quantification, SA-SBSE extracts showed a good standard curve by the standard addition method. We could quantify maltol, one of the most common potent aroma compounds in all samples, for various commercial samples by such high-throughput analysis.

Integrated water quality dynamics in Wadi Hanifah: Physical, chemical, and biological perspectives

The Wadi Hanifah, a crucial aquatic ecosystem, has unfavorable consequences from natural occurrences and human activities. Recognizing the critical need for sustainable water management, this study provides an in-depth evaluation of wadi water quality. A comprehensive assessment was conducted, analyzing physical properties (temperature, pH, electrical conductivity, turbidity, color, and odor), chemical constituents (nitrogen compounds, ion concentrations, heavy metals), and bacterial diversity. The study found significant temperature fluctuations, particularly in sun-exposed or stagnant water areas. The water exhibited slight alkalinity and variable electrical conductivity and turbidity, indicating differing pollution levels. High ammonia and heavy metal concentrations suggested organic and industrial contamination, respectively. In addition, the prevalent fecal-indicator bacteria pointed to possible sewage or agricultural runoff. The research highlights the complex interplay of natural and anthropogenic factors affecting Wadi Hanifah's water quality. It emphasizes the need for location-specific environmental management strategies focusing on pollution control and conservation to safeguard the wadi's ecological health. This study provides vital insights for effective water resource management in Wadi Hanifah, serving as a model for similar ecosystems.

Quick insights into whisky - investigating rapid and efficient methods for sensory evaluation and chemical analysis

The evaluation of aroma properties of beverages, both analytically as well as with human sensory studies, is a challenging task and most often related to time- and cost-intensive analyses. Whisky is a spirit offering a wide variety of aroma impressions caused by a complex mixture of aroma active compounds. In the present study, methods for the efficient evaluation of aroma characteristics are evaluated for 16 whisky samples of different origins (Scotch and American). Rate all that apply (RATA) was applied as a rapid method for the sensory evaluation of whiskies. Sensory evaluation of the samples led to the determination of eight significant aroma attributes: caramel-/cream caramel-/toffee-like, vanilla-like, (canned) peach-like, phenolic, smoky, fruity, flowery and (fermented) apple-/cider-like. Chemical analysis was conducted by stir bar sorptive extraction (SBSE) coupled to gas chromatography-mass spectrometry in combination with an in-house data processing tool for semi-automated analyte detection. Through chemical analysis of the whisky samples and automated compound detection, we report over 200 mostly aroma-active volatiles. To test both approaches for their potential for sample classification, a simple classification problem (Scotch vs. American) was applied. Linear discriminant analysis (LDA) indicates both that sensory evaluation by RATA (97.86%) and the applied analytical procedure (96.94%) are suitable for the distinction between the two whisky types. In both approaches, potential markers were determined for the classification. These investigations build a solid foundation for the implementation of a versatile platform facilitating rapid and efficient aroma evaluation in various foodstuffs and beverages.

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.

Volatile Organic Compounds: A Promising Tool for Bed Bug Detection

The recent decades' resurgence of bed bugs as a public health concern in industrialized countries has driven an increased interest on new sustainable insecticide-free methods to monitor and control these ectoparasites. Current methods of detection rely mainly on visual inspection or canine scent detection, which are methods that are time-consuming, require experience, are non-specific or require costly mission repetitions. Volatile organic compounds (VOCs) are considered an environmentally friendly alternative and a promising approach for bed bug detection. An overview of the released literature on VOCs, their chemical characteristics and their role in bed bugs' intra- and inter-species communications allowed us to highlight the identification of 49 VOCs in Cimex lectularius (23 molecules) and C. hemipterus (26), which are emitted by both sexes during diverse compartments including aggregation (46), mating (11), defense (4), etc., and all life stages including exuviae or dead bed bugs as a principal indicator of infestation. The latter has a great importance for application of these semiochemicals in successful detection and control management of bed bugs and to prevent their further dispersion. This approach has the advantage of more reliability compared to conventional detection methods with no need for repeated inspections, household furniture moving or resident rehousing for bed bugs' VOC detection, which are commonly performed by active or passive sampling with absorbing tubes and analyzed by gas chromatography-based analytical platforms.

Wire-tube DBD reactor for H2S treatment: optimization of geometric and electrical parameters

Based on the wire-tube DBD reactor, this paper studied the effects of different discharge lengths, discharge air gaps, and electrical parameters on the discharge characteristics of the DBD discharge module. The results show that under the condition of increasing applied voltage, different discharge lengths, discharge air gaps, thicknesses of the insulating medium, and equivalent capacitance of insulating medium all show an increasing trend, while the equivalent capacitance of air-gap medium fluctuated within a certain range. When the discharge length was 30 cm, the discharge air gap was 2 mm, and the thickness of the insulating medium was 1 mm, the discharge effect was the best. In terms of electrical parameters, with the increase of the applied voltage, the "burr" of the current waveform increased, the load voltage and discharge power also increased, the discharge air gap voltage remained almost unchanged, and the equivalent capacitance value of the insulating medium continued to increase while the equivalent capacitance of the air gap medium remained almost unchanged. The optimized DBD discharge module was used for the treatment of exhaust gas containing H₂S. The results show that when the gas flow rate was 80 L·h^-1, the initial concentration was 50 mg·m^-3, and the applied voltage was 65 V, the removal efficiency could reach 100% in 4 s. The energy efficiency analysis of the DBD discharge module shows that the energy efficiency of the discharge module varies by changing the different parameters; in the case of H₂S degradation, the end products were mainly SO₂ and SO₃.

Characterization of odour emissions in a wastewater treatment plant using a drone-based chemical sensor system

Conventionally, odours emitted by different sources present in wastewater treatment plants (WWTPs) are measured by dynamic olfactometry, where a human panel sniffs and analyzes air bags collected from the plant. Although the method is considered the gold standard, the process is costly, slow, and infrequent, which does not allow operators to quickly identify and respond to problems. To better monitor and map WWTP odour emissions, here we propose a small rotary-wing drone equipped with a lightweight (1.3-kg) electronic nose. The "sniffing drone" sucks in air via a ten-meter (33-foot) tube and delivers it to a sensor chamber where it is analyzed in real-time by an array of 21 gas sensors. From the sensor signals, machine learning (ML) algorithms predict the odour concentration that a human panel using the EN13725 methodology would report. To calibrate and validate the predictive models, the drone also carries a remotely controlled sampling device (compliant with EN13725:2022) to collect sample air in bags for post-flight dynamic olfactometry. The feasibility of the proposed system is assessed in a WWTP in Spain through several measurement campaigns covering diverse operating regimes of the plant and meteorological conditions. We demonstrate that training the ML algorithms with dynamic (transient) sensor signals measured in flight conditions leads to better performance than the traditional approach of using steady-state signals measured in the lab via controlled exposures to odour bags. The comparison of the electronic nose predictions with dynamic olfactometry measurements indicates a negligible bias between the two measurement techniques and 95 % limits of agreement within a factor of four. This apparently large disagreement, partly caused by the high uncertainty of olfactometric measurements (typically a factor of two), is more than offset by the immediacy of the predictions and the practical advantages of using a drone-based system.

Farmers and Local Residents Collaborate: Application of a Participatory Citizen Science Approach to Characterising Air Quality in a Rural Area in The Netherlands

In rural areas, livestock farming is a source of environmental concern. We describe a citizen science (CS) project in Venray, the Netherlands, where air quality was measured at livestock farms and surrounding residential premises. We used low-cost methods to measure air quality components and facilitated a dialogue between stakeholders about the results and solutions for cleaner air. PM_2.5 and PM₁₀ were measured using Nova Fitness SDS011 sensors, nitrogen dioxide (NO₂) and ammonia (NH₃) using Palmes tubes and odour annoyance was reported. Particulate Matter (PM) concentrations were higher close to layer farms, but elevated concentrations were limited at other farms and residential locations. NO₂ concentrations were elevated near busy roads, and higher NH₃ values were measured near livestock farms. Reporting of odour annoyance was limited, yet during the dialogue residents indicated that this was their largest concern. While both farmers and residents agreed with the general conclusions, they still preferred opposing measures. We conclude that characterisation of air quality using low-cost methods is possible, but expert guidance is needed. Moreover, education, commitment of participants and involvement of independent parties are crucial to ensuring a productive dialogue between stakeholders. The insights gained by participants and resulting dialogue were the greatest benefits of this CS approach.

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.

Application of Field Olfactometry to Monitor the Odour Impact of a Municipal Sewage System

Odorant emissions are associated with, among other things, wastewater transport in sewer networks; they contribute to air pollution and result in complaints from residents living close to emission sources. The critical location in terms of the formation of unpleasant odour compounds is the pressure line that connects the pumping station and the expansion well; this is where they are released into the atmosphere. This paper presents comprehensive results of olfactometric and chromatographic tests in the Polish city of Białystok using portable devices that allow for multiple determinations and instant results. The study attempts to investigate the relationship between odour and odorant concentrations and check the suitability of field olfactometry as a tool for the ongoing monitoring of the emission of noxious odours and for verifying complaints submitted by residents. Statistical analysis shows a very high correlation coefficient between cod and the concentrations of individual odorants, ranging from 0.82 to 0.91. This olfactometric research, mainly conducted in situ, can be an appropriate method for the ad hoc monitoring of processes in sewage networks. This method allows the detection of unwanted emissions of odours at individual points in the network in concentrations that are not detected by standard sensors but that nevertheless cause odour nuisances, complaints, and social conflict. The research results provide evidence in favour of the energetic usage of wastewater, which is in line with circular economy conception, since odour nuisance is one of its indicators.

Cannabis Cultivation Facilities: A Review of Their Air Quality Impacts from the Occupational to Community Scale

This review addresses knowledge gaps in cannabis cultivation facility (CCF) air emissions by synthesizing the peer-reviewed and gray literature. Focus areas include compounds emitted, air quality indoors and outdoors, odor assessment, and the potential health effects of emitted compounds. Studies suggest that β-myrcene is a tracer candidate for CCF biogenic volatile organic compounds (BVOCs). Furthermore, β-myrcene, d-limonene, terpinolene, and α-pinene are often reported in air samples collected in and around CCF facilities. The BVOC emission strength per dry weight of plant is higher than most conventional agriculture crops. Nevertheless, reported total CCF BVOC emissions are lower compared with VOCs from other industries. Common descriptors of odors coming from CCFs include "skunky", "herbal", and "pungent". However, there are few peer-reviewed studies addressing the odor impacts of CCFs outdoors. Atmospheric modeling has been limited to back trajectory models of tracers and ozone impact assessment. Health effects of CCFs are mostly related to odor annoyance or occupational hazards. We identify 16 opportunities for future studies, including an emissions database by strain and stage of life (growing cycle) and odor-related setback guidelines. Exploration and implementation of key suggestions presented in this work may help regulators and the industry reduce the environmental footprint of CCF facilities.

Micrometeorological Methods for the Indirect Estimation of Odorous Emissions

Odors are typically released into the atmosphere as diffuse emissions from area and volume sources, whose detailed quantification in terms of odor emission rate is often hardly achievable by direct source sampling. Indirect methods, involving the use of micrometeorological methods in order to correlate downwind concentrations to the emission rates, are already mentioned in literature, but rarely found in real applications for the quantification of odor emissions. The instrumentation needed for the development of micrometeorological methods has nowadays become accessible in terms of prices and reliability, thus making the implementation of such methods to industrial applications more and more interesting. For this reason, this work aims to provide an overview of micrometeorological methods and investigate their effective applicability to odors, thereby providing a short description of the physics related to such methods and analyzing the relevant scientific literature. The theoretical basis of these methods is presented, and their advantages and disadvantages are discussed. Moreover, their applicability to the estimation of odor emissions is discussed by providing some suggestions about the suitable ways to evaluate the most critical parameters needed for the calculation of the odor emission rate.

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.

CFD modeling of different mass transfer coefficients on hydrogen sulfide emission in a flux chamber

Hydrogen sulfide (H₂S) is commonly used as an indicator for odorous gas emission monitoring in wastewater treatment plants. The H₂S emission estimations can be performed using algebraic mathematical models or carrying out measurements at the source, with the dynamic flux chamber, for example. This work brings together these two methodologies in a computational fluid dynamics analysis. Fifteen liquid-phase mass transfer coefficient ([Formula: see text]) models were initially evaluated in establishing, at the liquid-gas interface in a flux chamber, an H₂S emission flux based on the friction velocity field from three different inlet flows (2, 5, and 10 L min^-1). Ten [Formula: see text] models were fully simulated, and the numerical results were compared with available experimental data. The higher the inlet flow, the higher the friction velocity at the interface, and the higher the H₂S emission. The H₂S emission was also strongly dependent on the constant coefficients of the existing [Formula: see text] models. Small variability on those coefficients generates considerable changes in emissions at the interface. Few and different models performed well in describing the available concentration data at the outlet sampling probe for different inlet flows, which shows there is still no single model capable of representing all simulated friction velocity ranges (0.005 to 0.017 m s^-1).

RHINOS: A lightweight portable electronic nose for real-time odor quantification in wastewater treatment plants

Quantification of odor emissions in wastewater treatment plants (WWTPs) is key to minimize odor impact to surrounding communities. Odor measurements in WWTPs are usually performed via either expensive and discontinuous olfactometry hydrogen sulfide detectors or via fixed electronic noses. We propose a portable lightweight electronic nose specially designed for real-time odor monitoring in WWTPs using small drones. The so-called RHINOS e-nose allows odor measurements with high spatial resolution, and its accuracy is only slightly worse than that of dynamic olfactometry. The device has been calibrated using odor samples collected in a WWTP in Spain over a period of six months and validated in the same WWTP three weeks after calibration. The promising results obtained support the suitability of the proposed instrument to identify the odor sources having the highest emissions, which may give a useful indication to the plant managers as regards odor control and abatement.

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A portable e-nose for real time odor quantification according to EN13725 is described

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The e-nose is installed on a small drone for dense spatial measurements

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The e-nose is demonstrated and validated in a wastewater treatment plant

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Errors in odor quantification are only slightly worse than dynamic olfactometry

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.

Recruiting, training and managing a sensory panel in odor nuisance testing

A growing level of environmental awareness in societies has led to an increased interest in the odor nuisance. Residents’ complaints have ultimately revealed the need to develop adequate research methods that would ensure objective measurements, thus shedding more light on this complex problem. One of the possible ways to conduct an objective odor assessment is to use a sensory panel in the tests. This paper presents the procedure for selecting and training the sensory analysis team especially for the purposes of odor nuisance testing. Several useful suggestions for conducting studies that involve a sensory panel are also provided. This in turn can prove a useful tool, supplementing the EN 13725 standard within the scope of building and convening a sensory panel. In this paper a performance comparison of two sensory panels is also discussed: one that received a basic training of 20h, solely based on the guidelines of the EN 13725 standard and another, whose members completed an extended training of 60h, based on the standards and guidelines used in the field of sensory analysis. It has been shown that acquiring more knowledge and developing certain competences in the field of sensory analysis may contribute to an increase in the overall and individual precision of determinations as adequate sensory training improves the panel’s performance.

Evaluation of Occupational Exposure Risk for Employees Working in Dynamic Olfactometry: Focus On Non-Carcinogenic Effects Correlated with Exposure to Landfill Emissions

This work aims to evaluate the non-carcinogenic health effects related to landfill odor emissions, therefore focusing on workers involved in dynamic olfactometry. Currently, the most common technique to quantify odor emissions is dynamic olfactometry, a sensorial analysis involving human assessors. During the analysis, assessors are directly exposed, at increasing concentrations, to odor samples, and thus to the hazardous pollutants contained therein. This entails the need to estimate the associated exposure risk to guarantee examiners’ safety. Therefore, this paper evaluates the exposure risk for olfactometric examiners to establish the minimum dilution level to be adopted during the analysis of landfills’ odorous samples to guarantee panelists’ safety. For this purpose, an extensive literature review regarding the pollutants emitted by landfill odor sources was conducted, comparing compounds’ chemical concentrations and threshold limit values (TLVs) to calculate the Hazard Index (HI) and thus establish a minimum dilution value. The data collected indicate that a non-negligible non-carcinogenic risk exists for all landfill emissions considered. However, from the data considered, the minimum dilution factor to be adopted is lower than the typical odor concentration observed for these sources. Therefore, the olfactometric analysis of landfill samples can be generally conducted in safe conditions.

Identification of origins and influencing factors of environmental odor episodes using trajectory and proximity analyses

It is challenging for the governmental agencies to provide an instant response and to systematically analyze the huge number of odor complaints which are received frequently by them. This study aimed to establish a data analysis framework featuring trajectory and proximity analyses to confirm odor origins, assess impact areas, and identify determinants and mechanisms of odor episodes based on odor reports. The investigation used 273 odor complaints reported in northern Collierville, Tennessee, between January 1st, 2019 and December 15th, 2020. The location of each complaint was geocoded in Google Map, and the backward wind trajectories were calculated using the web-based Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The nearby Eplex Landfill and Collierville Northwest Sewage Treatment Plant were targeted for the analyses. Odor impacts were evaluated with temporal and spatial characteristics of reported odor episodes. Logistic models were performed to identify weather parameters that significantly influenced odor occurrence. The field inspections indicated two periods targeting different sources. Period 1: from January 1st, 2019 to October 31st, 2020, the landfill appeared as the major source; Period 2: from November 1st, 2020 to December 15th, 2020, the sewage plant emerged as the major source. In Period 1, 65% of the complaints had wind transporting from the landfill, and 88% occurred at residences within 500 m of the landfill. In Period 2, 33% of the complaints had wind that blew from the sewage plant and 85% occurred at residences within 1000 m from the sewage plant. The likelihood of an odor episode day was significantly associated with wind speed [Odds Ratio (OR) = 0.66, 95% Confidence Interval (CI): 0.56-0.77], temperature (OR = 0.97, 95% CI: 0.95-0.98), and rainfall (OR = 1.02, 95% CI: 1.00-1.04). The odor issue in Collierville reflected poor zoning between the odor sources and residential areas. Separation distances of 500 m and 1000 m from the landfill and sewage facilities, respectively, are suggested to prevent odor issues. The proposed data analysis framework can be adopted by governmental agencies for fast responses to odor complaints, odor assessment, and environmental odor management.

Industrial odour pollution and human health: a systematic review and meta-analysis

OBJECTIVE

To conduct a systematic review to evaluate the association between residential or occupational short- and long-term exposure to odour pollution from industrial sources and the health status of the exposed population.

METHODS

The searches were conducted in Medline, EMBASE and Scopus in April 2021. Exposure to an environmental odour from industrial sources in population resident near the source or in workers was considered. We considered outcomes for which there was a biological plausibility, such as wheezing and asthma, cough, headache, nausea and vomiting (primary outcomes). We also included stress-related symptoms and novel outcomes (e.g. mood states). Risk of bias was evaluated using the OHAT tool. For primary outcomes, when at least 3 studies provided effect estimates by comparing exposed subjects versus not exposed, we pooled the study-specific estimates of odour-related effect using random effects models. Heterogeneity was evaluated with Higgins I2.

RESULTS

Thirty studies were eligible for this review, mainly cross-sectional (n = 23). Only one study involved school-age children and two studies involved workers. Only five studies reported odour effects on objective laboratory or clinical outcomes. Animal Feeding Operations and waste were the most common industrial sources. The overall odds ratios in exposed versus not exposed population were 1.15 (95% CI 1.01 to 1.29) for headache (7 studies), 1.09 (95% CI 0.88 to 1.30) for nausea/vomiting (7 studies), and 1.27 (95% CI 1.10 to 1.44) for cough/phlegm (5 studies). Heterogeneity was a moderate concern. Overall, the body of evidence was affected by a definitely high risk of bias in exposure and outcome assessment since most studies used self-reported information.

CONCLUSIONS

Findings underline the public health importance of odour pollution for population living nearby industrial odour sources. The limited evidence for most outcomes supports the need for high quality epidemiological studies on the association between odour pollution and its effects on human health.

Toxicological assessment method for evaluating the occupational risk of dynamic olfactometry assessors

The paper aims to propose a new method to evaluate the occupational exposure risk for examiners involved in dynamic olfactometry. Indeed, examiners are possibly exposed to hazardous pollutants potentially present in odorous samples. A standardized method to evaluate the examiners' occupational safety is not yet available and the existing models present some critical aspect if applied to real odorous samples (no uniform reference concentrations applied and presence of compounds for which no toxicity threshold is available). A deepening of assessment procedure to evaluate the occupation exposure risk for olfactometric assessors is necessary. This paper proposes a standardized approach for risk assessment in dynamic olfactometry. The proposed approach allows the quantification synthetic and conservative risk indices. In this model, the use of the hazard index for the odorous mixture was proposed to assess the non-carcinogenic risk; the calculation of the inhalation risk was applied to estimate the carcinogenic risk. Different databases can be used to retrieve proper occupational exposure limits, according to the proposed hierarchical basis. These implementations allow obtaining the complete characterization of real samples which can be used to calculate the minimum dilution factor for protecting the panellists' health.

Application of Machine Learning for Fenceline Monitoring of Odor Classes and Concentrations at a Wastewater Treatment Plant

The development of low-cost sensors, the introduction of technical performance specifications, and increasingly effective machine learning algorithms for managing big data have led to a growing interest in the use of instrumental odor monitoring systems (IOMS) for odor measurements from industrial plants. The classification and quantification of odor concentration are the main goals of IOMS installed inside industrial plants in order to identify the most important odor sources and to assess whether the regulatory thresholds have been exceeded. This paper illustrates the use of two machine learning algorithms applied to the concurrent classification and quantification of odors. Random Forest was employed, which is a machine learning algorithm that thus far has not been used in the field of odor quantification and classification for complex industrial situations. Furthermore, the results were compared with commonly used algorithms in this field, such as artificial neural network (ANN), which was here employed in the form of a deep neural network. Both techniques were applied to the data collected from an IOMS installed for fenceline monitoring at a wastewater treatment plant. Cohen’s kappa and Normalized RMSE are used as specifical performance indicators for classification and regression: the indicators were calculated for the test dataset, and the results were compared with data in the literature obtained in contexts of similar complexity. A Cohen’s kappa of 97% was reached for the classification task, while the best Normalized RMSE, namely 4%, for the interval 20–2435 ouE/m³ was obtained with Random Forest.

Non-carcinogenic occupational exposure risk related to foundry emissions: focus on the workers involved in olfactometric assessments

The scope of this work is the evaluation of the non-carcinogenic occupational risk related to foundry emissions, focusing on the category of workers involved in olfactometric assessments. Odor pollution from industrial activities such as foundries is a serious environmental concern. Sensorial techniques (e.g. dynamic olfactometry, EN13725:2003) currently represent the preferred method for odor emission characterization. During olfactometric analyses, human assessors are directly exposed to the odor at increasing concentrations, thus requiring the assessment of the associated exposure risk to guarantee workers' safety. This paper presents an investigation aiming to produce an inventory of compounds emitted from foundries together with their odor thresholds and toxicological limits (TLVs), with the final objective to propose a procedure for ensuring workers' safety during olfactometric analyses. Looking at the database resulting from this study, among the >100 compounds emitted by foundries, 8 have a maximum concentration above their TLV. Among those, ammonia, H₂S, phenol, toluene and trimethylamine, produce an odor stimulus before they reach a toxic concentration, thus not representing a risk for olfactometric workers. Benzene, formaldehyde and SO₂ are identified as the most critical compounds because they may reach toxic concentrations in foundry emissions, and they start being perceived by humans above their TLV. The proposed procedure entails a minimum dilution factor of 27'000 to be applied to odor samples analyzed by olfactometry, which however might result inapplicable in practical cases, thus pointing out the necessity to adopt chemical measurements to investigate specifically the concentration of the most critical compounds identified in this study.

Aerial Mapping of Odorous Gases in a Wastewater Treatment Plant Using a Small Drone

Wastewater treatment plants (WWTPs) are sources of greenhouse gases, hazardous air pollutants and offensive odors. These emissions can have negative repercussions in and around the plant, degrading the quality of life of surrounding neighborhoods, damaging the environment, and reducing employee’s overall job satisfaction. Current monitoring methodologies based on fixed gas detectors and sporadic olfactometric measurements (human panels) do not allow for an accurate spatial representation of such emissions. In this paper we use a small drone equipped with an array of electrochemical and metal oxide (MOX) sensors for mapping odorous gases in a mid-sized WWTP. An innovative sampling system based on two (10 m long) flexible tubes hanging from the drone allowed near-source sampling from a safe distance with negligible influence from the downwash of the drone’s propellers. The proposed platform is very convenient for monitoring hard-to-reach emission sources, such as the plant’s deodorization chimney, which turned out to be responsible for the strongest odor emissions. The geo-localized measurements visualized in the form of a two-dimensional (2D) gas concentration map revealed the main emission hotspots where abatement solutions were needed. A principal component analysis (PCA) of the multivariate sensor signals suggests that the proposed system can also be used to trace which emission source is responsible for a certain measurement.

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.

The Use of the Odor Profile Method with an “Odor Patrol” Panel to Evaluate an Odor Impacted Site near a Landfill

A third-party-trained “Odor Patrol” program was conducted at a school that is about a one-mile distance from a landfill to clarify the odor nuisance problems from the landfill. Every 20 min from 6 to 9 a.m. on school days, the “Odor Profile Method” (OPM) was used with the landfill odor wheel to identify the odor type and intensity of each odor type. This study showed that an Odor Patrol using the OPM can accurately define odor nuisance changes over time and can be used as a method to confirm changes of odor nuisances in a field study. The Odor Patrol only found 13 data inputs of the 1000 data inputs (1.3%) for the 100-day odor monitoring with a landfill odor or trash odor that could cause odor complaints. The Odor Patrol data and the Odor Complaint data compared well. The OPM by an “Odor Patrol” could determine the contribution of the nuisance odors from 6 to 9 a.m. at the school site, about one mile away from the landfill. The study demonstrated a novel approach for odor monitoring by using the Odor Profile Method with an Odor Patrol. The OPM not only confirmed the mitigation of a landfill odor problem, but it also determined odor character, odor intensity, odor frequency and odor duration during this study period. “Landfill gas” was determined to be primarily a rotten vegetable odor with a secondary sewery/fecal odor of lower intensity, and “trash odors” were primarily a rancid and sweet odor with a secondary sewery/fecal and/or rotten vegetable odor of lower intensities generated from trash reaching the landfill. The order of intensity observed from high to low was: Trash odor (Rancid–Sweet) > Rotten vegetable > Sewery/Fecal > Rancid. Thus, trash odor is the major problematic odor from the landfill site. Quality assurance methods were used to remove local odors from the evaluation.

Environmental chemical sensing using small drones: A review

Recent advances in miniaturization of chemical instrumentation and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications. The versatility of chemically sensitive drones is reflected by their rapid adoption in scientific, industrial, and regulatory domains, such as in atmospheric research studies, industrial emission monitoring, and in enforcement of environmental regulations. As a result of this interdisciplinarity, progress to date has been reported across a broad spread of scientific and non-scientific databases, including scientific journals, press releases, company websites, and field reports. The aim of this paper is to assemble all of these pieces of information into a comprehensive, structured and updated review of the field of chemical sensing using small drones. We exhaustively review current and emerging applications of this technology, as well as sensing platforms and algorithms developed by research groups and companies for tasks such as gas concentration mapping, source localization, and flux estimation. We conclude with a discussion of the most pressing technological and regulatory limitations in current practice, and how these could be addressed by future research.

GC-MS/O for the characterization of odours from cheese-production wastewater: A case study

The foul odour of cheese-production wastewater is a common problem in areas surrounding dairy wastewater treatment plants. For successful odour management, a better understanding of the key odorants and how to handle them during wastewater treatment is needed. This paper documents the results of using gas chromatography-mass spectrometry coupled with olfactometry (GC-MS/O) to analyze odours emanating from a possibly overloaded treatment plant in Czechia. Using a DB5 capillary column, 20 compounds were detected and identified, nonanal (FD_geomean 152) and octen-3-ol (FD_geomean 2048) having the most pungent odours.

Odour impact assessment by considering short-term ambient concentrations: A multi-model and two-site comparison

Short-term events are one of the specific aspects that differentiate odour nuisance problems from conventional air quality pollutants. Atmospheric dispersion modelling has been considered the gold standard to realise odour impact assessments and to calculate separation distances. Most of these models provide predictions of concentrations of a pollutant in ambient air on an hourly basis. Even when the hourly mean odour concentration is lower than the perception threshold, concentration peaks above the threshold may occur during this period. The constant peak-to-mean factor is nowadays the most widespread method for evaluating short-term concentrations from the long-term ones. Different approaches have been proposed in the scientific literature to consider non-constant peak-to-mean factors. Two prominent approaches to do so are the i) variable peak-to-mean factor which considers the distance from the source and atmospheric stability and the ii) concentration-variance transport. In this sense, the aim of this work is to compare the results of three different freely available dispersion models (namely, CALPUFF, LAPMOD and GRAL), which implement three distinct ways to evaluate the short-term concentration values. Two sites, one in Austria and the other in Italy, were selected for the investigation. Dispersion model results were compared and discussed both in terms of long-term (hourly) concentrations and short-term. An important outcome of this work is that the dispersion models provided more equivalent results for hourly mean concentrations, in particular in the far-field. On the contrary, the method to evaluate short-term concentrations can deliver disparate results, thereby revealing a potential risk of poor assessment conclusions. The utilistion of a multiangle methodological approach (dispersion models, study site locations, algorithms to incorporate short-term concentrations) allowed providing useful information for future studies and policymaking in this field. Accordingly, our findings call for awareness on how the use of a particular dispersion model and its sub-hourly peak calculation method can affect odour impact assessment conclusions and compliance demonstrations.

Odor Annoyance Assessment by Using Logistic Regression on an Example of the Municipal Sector

Around the sewage treatment plant, in the area affected by a large number of complaints about odor annoyance, field measurements of odor properties and questionnaires were carried out. It was confirmed that the inhabitants of the zone closest to the plant are most exposed to the smell, the most intense smell comes from the sludge dryer building, and smells from primary settling tanks and sediment plots are perceived as unpleasant. The analysis of surveys confirmed the problem of odor nuisance, especially in the immediate vicinity, where over 50% of respondents considered odor annoyance as extreme. A division of respondents was introduced into those experiencing severe nuisance and those for whom the smell was not annoying. Then, to relate the probability of occurrence of odor nuisance with a group of independent variables, logistic regression was used to describe the impact of independent variables on the dichotomous dependent variable. It has been shown that the likelihood of experiencing odor nuisance increases with the increase in the intensity of current odors, the parallel noise, and in people who focus on the existing smell, and decreases with increasing satisfaction with their health and in the case of regularly occurring odor.

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.

Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses.