Odour Dispersion Model Research Articles

Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets

Dispersion modeling is a useful tool for reproducing the spatial–temporal distribution of pollutants emitted by industrial sites, particularly in the environmental odor field. One widely used tool, accepted by regulatory agencies for environmental impact assessments, is the CALPUFF model, which requires a large number of input variables, including meteorological and orographical variables. The reliability of model results depends on the accuracy of these input variables. The present research aims to discuss a comparative study of odor dispersion modeling by initializing the CALMET meteorological processor with different input data: surface and upper air observational meteorological data, 3D prognostic data, and a blend of prognostic and measured data. Two distinct sources (a point and an area source) and two different simulation domains in Cuba and Italy are considered. The analysis of results is based on odor impact criteria enforced in some Italian regions by computing the 98th percentile of odor peak concentrations on an annual basis. For the area source, simulation results reveal that the ‘OBS’ and ‘HYBRID’ modes are largely comparable, whereas prognostic data tend to underestimate the odor concentrations, likely due to a reduced percentage of wind calms. For point sources, different input meteorological settings provide comparable results, with no significant differences.

Determination of separation distances integrating complaints records analysis and odour dispersion modelling in the Czech Republic

Dispersion models have proven to assist the development of regulation strategies for the mitigation of odour impact. Nevertheless, the complexity derived from the definition of the sources and the replication of the subjective perception of chemical mixtures raise the question whether it is enough to perform an assessment based exclusively on the predictions of models. Furthermore, there is still an ongoing debate on the most appropriate methodology to reproduce sub-hourly peak concentrations.With this in mind, the active participation of the affected community could help to identify better the processes that cause odour annoyance and tune the results obtained with the dispersion models. Recently, the AirQ application has been implemented in the Czech Republic to allow citizens to report odour episodes to the entity in charge. Hence, the goal of this work was to integrate the information collected from the complainants with the simulations from the Gaussian model SYMOS, and the Lagrangian models AUSTAL and GRAL. The evaluation was performed in three sites with different emission characteristics and terrain: a pig farm, a pet food producer, and an edible oil industry.The outcome of this approach allowed to evaluate the suitability of each model depending the characteristics of the source, compare the use of a constant peak-to-mean factor of 4 against the Concentration Variance Model, and determine the applicability of certain odour impact criteria (OIC) for establishing separation distances.

Odour dispersion modelling, impact criteria, and setback distances for an oil refinery plant

Odour dispersion modelling is considered to be the most effective approach in evaluating odour impact of various air emission sources as field odour measurements are associated with several difficulties. Using odour impact criteria (OIC), odour dispersion modelling could provide a comprehensive map of impact for current and future sources, along with providing time and cost savings, yet there was very little research on odour dispersion modelling for oil refineries. The OIC are source-specific but OIC for oil refineries has not been well studied. This study investigated odour impact of a conventional oil refinery in Western Canada using the AERMOD model. Odour intensities (OIs) were measured at 62 spots downwind from 70 m to 6.7 km from the plant. Peak to Mean factors (PMF) were generated for the model. The measured OIs were converted to odour concentrations (OC) using OI-OC correlation. Comparison of modelled and measured data indicated that AERMOD predicted the OC satisfactorily (r = 0.67). The model was then used to predict odour impact for this area based on 5-year meteorological data. Comparing dispersion modelling results with the general OIC of the region and odour complaint data, a new set of OIC for the oil refinery was proposed, and the resultant appropriate setback distances for different land uses were predicted by AERMOD. The OIC proposed by this study may be applicable for other refineries yet potential differences in odour acceptance levels of other communities should be considered. The resultant setbacks from this study may not be applicable for sites with different topographic and meteorological conditions.

New International Handbook on the Assessment of Odour Exposure Using Dispersion Modelling

A new development towards the first worldwide guideline on the assessment of odour exposure by using dispersion modelling is taking its first steps. At this stage, there are many initiatives around the world related to odour dispersion modelling but there is no specific handbook or guidance document for odour modelling to our knowledge. Modelling odours is complex and many of the guidelines on modelling published around the world fall short in treating this vector. Odour modelling often requires forgetting traditional dispersion modelling operating modes and focusing on exposure. Odours are perceived in seconds or minutes, not hours, and this is key in calculating their impact in the ambient air. Most odour incidents are generated during calm or very low wind speeds which do not facilitate the dispersion of an odour and that makes modelling extremely challenging.Development of this guideline is an initiative promoted by over 50 experts around the globe in the area of modelling odours. The group is led by Carlos Diaz (Spain), Jennifer Barclay (New Zealand) and Gunther Schauberger (Austria). The first meeting took place in August 2020, and there are planned monthly meetings. The aim of this paper is to report on the advances being made for this initiative.

Advanced Data Mining for Odour Emissions Monitoring: Experimental Peak-to-mean Calculations and Spectral Analysis of Data Derived from Ioms in Two Waste Treatment Plants

A landfill for non-hazardous waste and a composting plant were equipped with an integrated odour management system made up of instrumental Odour monitoring systems and automated air samplers. Collected data by the instrumental system cover a time span of 8 months and 9 months for the two plants, respectively, and have been used for investigating spatial distribution and statistical properties of peak-to-mean ratios; moreover, a new method of data analysis, not yet employed in the field of odour dispersion modelling, has been proposed. A peak-to-mean value is defined as the ratio between an hourly mediation time and the average duration of a single human breath (few seconds). Data derived from 0.033 Hz sampling frequency by IOMS were used to calculate peak-to-mean for the monitoring period for both plants; the size of samples analysed allowed the implementation of a statistical calculation methodology for peak-to-mean estimation, based on the construction of empirical probability density functions (PDF). Moreover, data collected from instruments installed in different areas of the plants, were used to evaluate how PDF of peak-to-mean values varies with the distance from the odour emission sources. The same data were also used to develop a new method of analysis based on time series processing through the use of the Fourier Transform. Spectral analysis, sometimes used for meteorological variables, more recently has been applied in the study of air pollution data, although this type of analysis makes a decisive contribution to the understanding of cyclical behaviour in the observed data and provides information on temporal and spatial scales of such mechanisms. The aim is therefore to define a viable methodology to time series of odour concentration data collected at the fence of a plant, to be used to gather information on periodic odour variations. Interesting results, which related some periodic behaviours in odour emissions to working periods and composting phases, were observed, demonstrating the usefulness of such methodology to assess whether odour fluctuations are caused by atmospheric conditions variability or plant management operations.

Summary and Overview of the Odour Regulations Worldwide

When it comes to air pollution complaints, odours are often the most significant contributor. Sources of odour emissions range from natural to anthropogenic. Mitigation of odour can be challenging, multifaceted, site-specific, and is often confounded by its complexity—defined by existing (or non-existing) environmental laws, public ordinances, and socio-economic considerations. The objective of this paper is to review and summarise odour legislation in selected European countries (France, Germany, Austria, Hungary, the UK, Spain, the Netherlands, Italy, Belgium), North America (the USA and Canada), and South America (Chile and Colombia), as well as Oceania (Australia and New Zealand) and Asia (Japan, China). Many countries have incorporated odour controls into their legislation. However, odour-related assessment criteria tend to be highly variable between countries, individual states, provinces, and even counties and towns. Legislation ranges from (1) no specific mention in environmental legislation that regulates pollutants which are known to have an odour impact to (2) extensive details about odour source testing, odour dispersion modelling, ambient odour monitoring, (3) setback distances, (4) process operations, and (5) odour control technologies and procedures. Agricultural operations are one specific source of odour emissions in rural and suburban areas and a model example of such complexities. Management of agricultural odour emissions is important because of the dense consolidation of animal feeding operations and the advance of housing development into rural areas. Overall, there is a need for continued survey, review, development, and adjustment of odour legislation that considers sustainable development, environmental stewardship, and socio-economic realities, all of which are amenable to a just, site-specific, and sector-specific application.

Analysis of Separation Distances under Varying Odour Emission Rates and Meteorology: A WWTP Case Study

A wide variability of odour impact criteria is found around the world. The objective of this research work was to evaluate the influence of the uncertainties related to some individual stages of odour impact assessment in the application of regulatory criteria. The evaluation procedure was established by following the guidelines of the Northern Italian regions. A wastewater treatment plant located in Northern Italy was considered as a case study. Odour dispersion modelling was carried out with the CALPUFF model. The study focused on two phases of the assessment. The first phase was the selection of the meteorology datasets. For low odour concentration thresholds (CT = 1 OU m−3), the results showed that two different years (2018 and 2019) provided similar patterns of the separation distances. The difference between the two years tended to increase by increasing the value of the concentration threshold (CT = 3 OU m−3 and CT = 5 OU m−3). The second phase of the assessment was the selection of the open field correction method for wind velocity used in the calculation of odour emission rates (OERs). Three different relationships were considered: the power law, the logarithmic law and the Deaves–Harris (D–H) law. The results showed that OERs and separation distances varied depending on the selected method. Taking the power law as the reference, the average variability of the separation distances was between −7% (D–H law) and +10% (logarithmic law). Higher variability (up to 25%) was found for single transport distances. The present study provides knowledge towards a better alignment of the concept of the odour impact criteria.

INFLUENCE OF MODELLING CHOICES ON THE RESULTS OF LANDFILL ODOUR DISPERSION

Landfills are an important source of odour pollution, potentially causing nuisance to adjacent populations. The most commonly used odour impact assessment for this type of plants usually involves a combination of dynamic olfactometry with dispersion modelling. Despite the advantages associated with the use of dispersion models, there are still some important issues related to their uncertainty. The dispersion model requires the Odour Emission Rate (OER) as input, expressed as units of odour emitted per unit time. Source term characterization and the estimation of the OER are typically the most important steps in the model’s implementation, accounting for the highest contribution to the overall uncertainty. Another important element of uncertainty when modelling emissions from landfill surfaces is the geometrical implementation of the emission source in the dispersion model. This entails the definition of the initial dimensions of the emission, which is critical in the case of large area sources. This paper discusses issues related to uncertainty in the use of dispersion models for the evaluation of landfill odour impacts, particularly focusing on the estimation of the OER and the emission’s initial vertical dimension. This study shows that modelling choices may lead to a variance in the resulting modelled odour concentrations at receptors differing by up to a factor 3. This variability should not cause distrust in the method, but rather indicates the importance of having odour dispersion modelling studies carried out by experts with deep knowledge of the physical-chemical mechanisms underlying atmospheric emissions.

Evaluation of a field olfactometer in odour concentration measurement

Field olfactometry using portable olfactometers is one of the common methods in assessing odour nuisance of odour sources and validating odour dispersion modelling results. To obtain reliable field odour data, a field olfactometer needs to be accurate and reliable. The IDES Scentroid SM 100 is a recently developed portable field olfactometer but its accuracy in measuring odour concentration has not been sufficiently investigated. In this study, the results obtained by SM 100 are compared with the results obtained by a standard laboratory olfactometer in measuring odour concentrations of samples containing n-butanol gas and poultry barn exhaust air. The standard olfactometer used was designed according to European EN 13725 Standard (2003) and was a triangular forced-choice olfactometer that could use 8 panellists simultaneously for olfactometry analysis. Results showed that there was a good agreement between odour concentrations obtained by these two types of olfactometers for full-strength and diluted samples with odour concentrations ranging from 50 to 850 ou m−3 (P > 0.05). SM 100 gave 28%–41% higher values than the laboratory olfactometer for poultry barn odour, while this was not observed for n-butanol. For n-butanol samples, SM 100 gave 53% and 9% higher values for the original and 16 times diluted samples, respectively; in contrast, SM 100 gave 9% lower values for 64 and 128 times dilutes samples. Different discrepancy trends in two tested odour types suggest that more odour types should be tested to provide a better picture of the level of accuracy of SM 100.

Modeling of Odor from a Particleboard Production Plant

Production of particleboards is associated with volatile organic compounds emissions, many of which have odorous properties. The objective is to determine whether the odor emissions from the production, using spruce and aspen as raw material, comply with the local odor limit defined in a specific way—an hourly average odor concentration leading to 16% odor annoyance and not being exceeded more than 2% of the time in residential area. Complex data of odor are presented from two largest sources—dryer and press, providing odor emission concentration, flow, composition, and dispersion model. The odor emission flow from the dryer is by factor of 19 higher with the value of 7655 × 106 ouE h−1 than that from the press. The dependence between odor concentration and annoyance is defined and determined. Identifying the top 10 odorous compounds (the most relevant are hexanal, butanoic and acetic acids, and α- and β-pinenes) in the emissions, the concentration, which leads to 16% annoyance, is quantified to 0.62 µg m−3. The dispersion model provides odor distribution, proving that the given limit is not exceeded in the nearby residential area.

Application of mathematical modelling in evaluation of odour nuisance from selected waste management plant

A wide range of tools is required to comprehensively assess the impact of odours on people and the environment. Among them, mathematical tools for odour dispersion modelling are widely used. We can distinguish models based on Gaussian, Lagrangian and Eulerian approach. In Poland, the reference model, Operat FB, based on Gaussian way is commonly used for odour dispersion modelling. Modelling was conducted for two scenarios: first including area and point emission sources, while second one included only point sources. Comparison of both scenarios shows significant improvement of odour impact range of selected Plant in case when area sources are excluded.

Odour impact assessment of a large municipal solid waste landfill under different working phases

<p>Odours are among the main causes of complaints in regards to environmental issues for a variety of plants, including landfills. The emissions from landfills can affect the quality of life and negatively influence the area nearby. To protect people living in the surroundings from excessive odour exposures, different environmental protection practices may be implemented. In order to optimize technical and economic aspects, various configurations should be taken into account. In this view, the odour dispersion modelling represents a suitable tool to simulate different scenarios. In the present study, the odour impact from a large landfill located in Borgo Montello (Lazio Region, Italy) has been assessed by a dispersion model. Different operating conditions were simulated, in view of minimizing the odour annoyance during the phase of temporary closure of an area of the investigated plant. The Calpuff model was selected as it is well recognized among the preferred models for assessing the long range transport of pollutants. The Odour Emission Rates, inputs to the model, were calculated based on the results of dynamic olfactometry. The outputs from the dispersion model were investigated to define the best measures for the control of the odour emissions during the most impactful operations.</p>

Update on the Italian Regulation about Odour Emissions and Impact and Future Perspectives

Recently (Decreto Legislativo n. 183 issued on 15/12/2017) a new article has been inserted in the national Environmental Act in force in Italy. Its title is Emissioni odorigene (i.e. Odour emissions) and it represents a step forward for the national regulation about odour in Italy, where just local (regional) regulations had been issued before.The new regulation (Decreto Legislativo 152/2006, article 272-bis) preserves the status quo: in order to prevent and control odour emissions from industrial facilities, the Regions have the permission to establish regional regulations about odour emissions and to introduce requirements in permits (permit conditions) about odour emissions.In order to harmonize the differences between regional regulations about odour emissions, the second paragraph of article 272-bis opts for coordination among Regions by means of the existent national working group dealing with air quality (established by the national law adopting the EU Air Quality Directive). According to article 272-bis, this working group, constituted by members from Regional Administrations, can set specific emission limit values about odours and define methods for monitoring and assessing odour impacts. While some differences among regional regulations will be unavoidable as far as emission limit values and acceptable odour impact criteria are concerned, methods and procedures for monitoring emissions and assessing odour impact should be hopefully similar over the entire national territory. Therefore many operators in the Italian market (e.g. big companies owning several facilities over the national territory, laboratories, consultancy companies) need for a technical regulation for topics not covered by European standards (e.g. odour dispersion modelling, odour management plans). Italian standardization body (UNI) is proposing some new work items in order to elaborate new technical standards serving as bases for monitoring and control odour emissions and odour impact in fields not covered by the published European standards.

Full-scale composting of sewage sludge and market waste: Stability monitoring and odor dispersion modeling

The aim of this study was to assess the odor immission derived from full-scale composting of different abundant and highly pollutant organic waste: sewage sludge with bulking agent (SL), sewage sludge pretreated through anaerobic digestion and supplemented with bulking agent (SL-AD), and market waste with olive leaves (MW-OL). The combination of dynamic olfactometry and Gaussian dispersion modeling allowed both the quantification of odor emissions from each waste and the evaluation of their global odorous impact in nearby urban areas. Wind speed, summer and winter seasons, and atmospheric conditions were considered in the dispersion model. The results revealed that high wind speed (2.6 m/s) increases the global odor immission in summer season, independently of atmospheric stability. However, the maximum odor immission concentration recommended for composting process was not exceeded in any case, which depends on each country/region. The experimental results also enable to evaluate the influence of several physico-chemical variables on odor emissions derived from composting. The removal of nitrogen and volatile solids was the main cause for odor generation. Moreover, the microbiological activity of each substrate was monitored throughout the process and different percentages of biodegradability were quantified depending on the type of substrate and pretreatment applied.

Improved impact assessment of odorous compounds from landfills using Monte Carlo simulation

Landfills are city infrastructures used for the treatment of municipal solid waste (MSW) in China. However, due to technical failure and/or management problem most of them are facing serious secondary pollution such as groundwater contamination and odor nuisance. The latter is the main reason causing a growing number of public complaints. Atmospheric dispersion models are routinely adopted for odor impact assessment, but these models provide deterministic predictions only. To determine the potential odorant paths and treat the uncertainty of odor pollution, Monte Carlo simulation coupled with an odor dispersion model was proposed and named Monte Carlo-dispersion simulation method (MCDSM). By introducing a series of random values of error components in the dispersion model, MCDSM can produce probabilistic odor impact results. Values of these variances were randomly selected according to their probability density functions (PDFs) due to the imprecise knowledge of the meteorological and emission conditions. After running the odor dispersion model for numerous times, the randomization produces a set of possible results that closely resembles the expected behavior of the odorants.This study applied MCDSM to estimate the odor impact of methyl mercaptan (CH3SH) on an MSW landfill in Beijing, China. The PDF of the CH3SH emission rate was derived from the field data. The uncertainty of odor impact was analyzed statistically, and the results were summarized using the probability of odor exceedance (POE). A POE map of CH3SH was plotted for a particular interest, in which the north downwind direction was the most polluted area. MCDSM provides a scientific approach for the assessment of odor pollution from individual odorant, which can benefit the formulation of standard for odor impact assessment in landfill sites.

Combination of field inspection and dispersion modelling to estimate odour emissions from an Italian landfill

In the case of landfills, the determination of odour emissions is particularly complex. Up to now, no universally accepted methodology for sampling and assessing emissions from landfill surfaces has been established. Besides, the dependence of such emissions from some crucial environmental variables, such as wind speed, has not been univocally defined yet. In this study, odour dispersion modelling and plume inspections by human assessors were combined in order to estimate the odour emissions from a landfill in Southern Italy. Two substantially different approaches were compared: the one that considers emissions as a function of the wind speed blowing over the surface, counterposed to the one that considers odour emissions as constant, in agreement with the most recent studies on the matter. The comparison of the field assessments and the model clearly highlights that the first approach significantly overestimates the landfill odour emissions, whereas the use of a constant odour emission rate results in a much better correspondence between model outputs and field assessments, both in terms of shape and extension of the determined odour plume extents.

Odor dispersion modeling with CALPUFF: Case study of a waste and residue treatment incineration and utilization plant in Kocaeli, Turkey

ABSTRACTThis study addresses the odor problem at a waste and residue treatment incineration and utilization plant located within the borders the Alikahya district of the Kocaeli province in Turkey. In the first stage of the study, odor measurements were made at designated sampling points, while in the second stage, odor concentrations were determined at the receptor points through dispersion modelling using a USEPA (United States Environmental Protection Agency) certified long-range (>50 km) CALPUFF lagrangian puff model. In the final stage, an analysis of the predicted and observed values was carried out using such statistical methods as geometric mean bias (MG), geometric variance (VG) and fractions of predictions within a factor of two observations (FAC2).During the modelling study, the highest one-hour concentration level was found to be 1868.10 OU (Odor Units), and the 24-hour concentration level was found to be 1316 OU, representing a decrease of approximately 30 percent. According to the measurement made, the maximum concentration value was 2455 OU. Odor measurements were carried out at 13 points within the area in order to assess the prediction results. When the results were assessed using the MG, VG and FAC2 statistical methods, it was observed that an acceptable model performance was not achieved for the whole sampling point. When the reason for this was investigated, it was concluded that the observed values were lower than the predicted values due to the fact on the measurement days, the odor was dispersed by wind. It was further concluded that the observed values were higher than the predicted values as a result of odors emitted by other plants in the area. When the measurements in residential areas were examined to identify the effect of the odors, it was determined that although the primary density of settlement is to the southwest of the plant, it was not this area that was affected most, but rather the area to the northeast, where there is a lower settlement density.

Odour Dispersion Modelling for Raw Rubber Processing Factories

An odour dispersion modelling approach was adopted to assess odour dispersion, particularly from the drying stage of raw rubber processing factories, identified as a major source of malodour pollutant in the rubber industry. The Ausplume modelling software which uses a Gaussian plume dispersion model was used to predict odour dispersion from the water scrubber, a malodour control system used by rubber processing factories. The modelling involved a selected range of parameters in correlation with characteristics of raw rubber malodour pollutants and interpretations of the predicted data were discussed in terms of effects of stack height of the water scrubber, odour concentration and local meteorological conditions at several locations in peninsular Malaysia. In general, it was found that a higher stack of water scrubber minimises effects of different emission concentration characteristics and local meteorological conditions. This was demonstrated by profiles of predicted odour concentration dispersion and minimal dispersion variation within variables as attributed by standard deviation curves. Furthermore, higher stacks predicted lower odour concentration carried over from the point of emission to ground level receptors by taking into consideration all of the modelling assumptions. In summary, the initialfindings from this work are beneficial to designing a better odour control system which subsequently minimises odour impact to the surrounding environment.

Determining Separation Distances to Avoid Odour Annoyance With Two Models for a Site in Complex Terrain

Direction-dependent separation distances to avoid odour annoyance, calculated with the Gaussian Austrian Odour Dispersion Model AODM and the Lagrange particle diffusion model LASAT for a site in complex terrain, will be analysed and compared. The relevant short-term peak odour concentrations are calculated with a stability-dependent peak-to-mean algorithm. For both models, the same emission and meteorological data, but model-specific atmospheric stability classes have been used. The estimate of atmospheric stability is obtained from three-axis ultrasonic anemometers using the standard deviations of the three wind components and the Obukhov stability parameter. In addition, separation distances are also determined for an odour threshold of 0.25 (factor 4) of the German TA Luft (Technical Guideline for Clean Air) uniquely applied over all stability conditions and distances. The results are demonstrated for the Austrian village Weissbach in the narrow Saalach valley.Meteorological data at this site are provided by a three-axis ultrasonic anemometer from which also stability information (Obukhov stability parameter, OSP) is directly deduced. Separation distances are determined fortwo odour impact criteria, namely exceedence probabilities of 3 and 8 %, each in combination with the odour threshold of 1 ouE/m3. The form of the separation distances around the fictitious odour source depends strongly on the prevailing meteorological conditions (wind and stability), on the odour impact criteria and on the peak-to-mean factors.

The modelling of odour dispersion as a support tool for the improvements of high odours impact plants

ABSTRACTTwo scenarios in terms of odour impact assessment were studied during the phase of upgrading of an existing waste treatment plant: CALPUFF was used for the simulation of odour dispersion. Olfactometric measures, carried out over different periods and different positions in the plant, were used for model calibration. Results from simulations were reported in terms of statistics of odour concentrations and isopleths maps of the 98th percentile of the hourly peak concentrations, as requested from the European legislation and standards. The excess perception thresholds and emissions were utilized to address the plant upgrade options. The hourly evaluation of odours was performed to determine the most impacting period of the day. An inverse application of the numerical simulation starting from defining the odour threshold at the receptor was made to allow the definition of the required abatement efficiency at the odours source location. Results from the proposed approach confirmed the likelihood to adopt odour dispersion modelling, not only in the authorization phase, but also as a tool for driving technical and managing actions in plant upgrade so to reduce impacts and improve the public acceptance. The upgrade actions in order to achieve the expected efficiency are reported as well.