Everett/Marysville Regional Odor Monitoring Project

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 Regional odor project  The Everett – Marysville area has a complex olfactory landscape. Several industrial and municipal activities located nearby generate odors that could, in some operational and meteorological circumstances, be perceived by the surrounding communities. Amongst the sources of odor generation, there are a number of contributors including: a composting plant, top soil facility, a wood yard and chip processing, landfill and 3 WWTP.

   Many of these odors have similar character. To better understand how the odors are being produced and the best ways to reduce them, the Local Air Agency started an odor monitoring project in the area.

Thierry Pagé1 and Raymond C. Porter2

1 CEO, Odotech, 3333 Queen-Mary bur 301, Montreal, QC, Canada, H3V1A2,

2 Senior Air Quality Meteorologist and knowledge leader, Odotech, Reading, MA, 01867-1224 USA

 

   Competing interests: The author has declared that no competing interests exist.

   Academic editor: Carlos N Díaz.

   Content quality: This paper has been peer reviewed by at least two reviewers. See scientific committee here

   Citation: T Pagé and R. C. Porter, Everett/Marysville Regional Odor Monitoring Project, Ist International Seminar of Odours in the Environment, Santiago, Chile, www.olores.org

   Copyright: 2014 olores.org. Open Content Creative Commons license., It is allowed to download, reuse, reprint, modify, distribute, and/or copy articles in olores.org website, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

   Keyword: Regional Odor Impact, Odor Committees, Integrated Community Odor Monitoring, Electronic Nose, Olfactometry, Odor sampling, Regional Odor Assessment

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Abstract

   The Everett – Marysville area has a complex olfactory landscape. Several industrial and municipal activities located nearby generate odors that could, in some operational and meteorological circumstances, be perceived by the surrounding communities. Amongst the sources of odor generation, there are a number of contributors including: a composting plant, top soil facility, a wood yard and chip processing, landfill and 3 WWTP. Many of these odors have similar character. To better understand how the odors are being produced and the best ways to reduce them, the Local Air Agency started an odor monitoring project in the area. Through this project automatic odor monitoring devices are installed to collect real-time information about odors: types of odors, when they are occurring, and what’s causing them. PSCAA is also gathering odor information from participating residents. Having neighbors participate in the characterization of the odors leads to a greater sense of community involvement in the main aspects of the project. This also fosters a greater understanding of odor quantification by local residents while establishing a more constructive dialog between the plants’ management and neighboring residents. The program methodology involves four steps: Planning of the study and data management system development; Selection, information and training of the observer residents; Study supervision and follow-up; Statistical treatment of the results collected. This paper presents the methodology used to set up the project, implementation phases and results of the 12 Months of community and automated monitoring.

Introduction

   The Puget Sound Clean Air Agency (PSCAA) is responsible for the air quality in King, Kitsap, Pierce and Snohomish counties in Washington State. The Agency is currently trying to improve their understanding of odor events that are occurring in the Everett and Marysville region, north of Seattle.

   The Everett – Marysville area has a complex olfactory landscape. Several industrial and municipal activities located at the Smith Island and nearby, generate odors that could, in some operational and meteorological circumstances, be perceived by the surrounding communities. Additionally, the delta area could potentially generated odors from natural origin that could also be categorized as unpleasant. The region is likely to have complex wind distribution because of the presence of the shore interacting with the prevailing winds. Several communities are impacted by the odors. The next figure highlights the residential areas mainly impacted by the industrial and municipal activities.

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Fig. 1:Areas where odors are reported from complaint log book and potential odor sources (red circles).

   To better understand how the odors are being produced and the best ways to reduce them, the PSCAA started an odor monitoring project in the area. Through this project automatic odor monitoring devices are installed to collect real-time information about odors: types of odors, when they are occurring, and what’s causing them. PSCAA is also gathering odor information from participating residents.

Objectives

   The combined data aims to help stakeholders and community to better understand what is impacting the community and identify solutions. The objectives of this large scale study were to

  • Improved factual information
  • Proactive response of the authorities to the region’s concerns
  • Faster response - Better flow of information - Increase in credibility
  • Improved community relations
  • Improved quality of life and less stress
  • Increased understanding of emissions sources contribution to emissions
  • Provide factual and quantitative information to improve the management of plant operations aiming to reduce the odor events. Use of objective science to generate data to select cost-effective odor reduction solutions at key location in plant processes

Materials and methods

   The project has three main parts. One was an audit of the potential odor sources in the area. The second part was creating a committee of volunteer community members, who are trained to help identify and distinguish odors. They log their observations of the kinds of odors they are experiencing, when they are noticing odors, and the impact of those odors. In the third part of the project, a regional odor monitoring system was set up with electronicnoses installed at facilities identified in the audit. The e-noses are connected to a regional real-time dispersion modeling system based monitored sources and local weather stations. The e-noses continually monitor odors generated by these facilities, providing real-time information about when and where odors are occurring.

Phase 1: The objectives of the audit were to identify and validate:

  • potential odor sources from industrial and municipal sites
  • fugitive/ channeled emissions
  • waste parameters such as stack height, emission rates, diameter, flow rates
  • mapping of complaints (neighborhoods)
  • develop recommendations for identified odor sources
  • identify facilities then meet with facilities of identified odor sources to review the odor monitoring program, solicit feedback on the preliminary characterization and coordinate possible follow-up sampling for a quantitative odor characterization.
  • define parameters for the Odor observer committee
  • inventory odor sourcesand identify operating parameter of all processes
  • locate sensitive neighbourhood & communities
  • select location for weather towers
  • rank odor sources
  • gather land use and landscape information

Phase 2: Regional Continuous Odor Monitoring System:

   Following the audit, the information gathered permitted to design, develop,installand operate a community odor monitoring program, which includes the following elements:

  • Continuously monitors odor source emission concentrations measured in odor units per cubic meter (OU/m3) at point of source of source or other specified locations.
  • Calculate odor source emission rates (point and/or area sources) in a manner that produces odor emission rates for dispersion modeling.
  • Continuous monitoring system is be based on the electronic nose technology and calibrated by olfactometry using EN13725.
  • Real time meteorological data needed to support the community odor monitoring program.
  • Continuous dynamic dispersion modeling that integrates the available real time odor emission rate information along with real time meteorological data to produce updated results every 4 minutes. Dispersion modeling information are presented in a manner that shows resolution of individual source plume concentrations within the facilities, in the surrounding area, and at any specific receptor points identified for the community’s use. It also shows the combined impacts of all source plume/emission contributions in the model. The model uses EPA model AERMOD.

   In short, the objective of the system was to provide a fully automated Odor monitoring & measurement system with real-time dispersion modeling for multiple sites, centralized in one location. The system provides instantaneous information to permit to work with all stakeholders in establishing best odor practices, understanding the dynamics of odor episodes while maintaining an archive of the overall regional odor makeup. The OdoWatch® system was chosen for this project. It combines a half-dozen pieces of software and technology to help create a detailed and accurate picture of the odor emissions from a site, as shown in Figure 2. First, a series of electronic noses or eNoses can analyze the air for odors, using a sensor matrix that mimics the behavior of the human nose. The eNoses are positioned near the odor sources of the site and measure the odor continuously. The odor data from the eNoses and the local weather data are sent to the pre-configured OdoWatch® software, which models the atmospheric dispersion and then generates a map of the odor plume, overlaid onto a map of the site and the surrounding area. OdoWatch® is calibrated to recognize and quantify (in odor units) the odors of each site.

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Fig 2 :OdoWatch System

   This approach offers a series of unique advantages over less comprehensive odor detection techniques. Not only do the eNoses provide quantifiable measurements of odor levels at one given location, but the system can use atmospheric dispersion modeling to show the full extent and intensity of an odor plume. An odor monitoring system like OdoWatch® combines automatic central monitoring of odor emissions with a clear display showing odor concentration that incorporates current weather data and is updated every 4 minutes.

   For this project, two meteorological stations were installed: one on Smith Island at Cedar Grove and one at the Marysville air quality monitoring station near the Totem Middle School in Marysville. The meteorological data were continuously collected, averaged over a 4-minute period and entered into the dispersion model within the OdoWatch® software.Olfactometric analyses were performed by St. Croix Sensory located in Stillwater, Minnesota, a third-party odor laboratory selected by PSCAA. At total, 15 individual municipal and industrial facilities were included in the regional odor monitoring system and more than 64 individual odor sources modelled continuously.

Phase 3: Integrated Community Odor Committees (ICOC)

   The main objectives of the ICOC are to involve the community in assessing the odor-air quality, determine the odor impact on nearby residents, identify sources of odor pollution and assess the performance of implemented solutions. This is achieved by getting residents to participate in the monitoring of odor. Data gathered could be used by plant managers to create effective site operation management in order to minimize their odor impact.

   Having neighbors participate in the characterization of the odors leads to a greater sense of community involvement in the main aspects of the project. This also fosters a greater understanding of odor quantification by local residents while establishing a more constructive dialog between the plants’ management and neighboring residents.

   The ICOC program methodology involves four steps:

  • Stage 1 – Planning of the study and data management system development;
  • Stage 2 – Selection, information and training of the observer residents;
  • Stage 3 – Study supervision and follow-up;
  • Stage 4 – Statistical treatment of the results collected.

Results and discussion

   Having committee members quantify the date, time, location, character, intensity, hedonic tone of the observed odors along with the weather conditions at the time of the observation, greatly enhances the quality of the information provided.Every odor observation submitted was useful to the study and added to the understanding of odors in the region.A total of 161 observations were received between October 2012 and November 2013. Close to 63% of them were received between May and July 2013. Approximately 60% of the odors perceived were attributed to composting and 21 percent to fresh waste. Most observations were between 9AM and 1PM or between 4PM and 10PM and reported duration of 30 minutes or less.Observations reported odors as unpleasant or very unpleasant in 88%. Of the composting odors, 69% were reported as unpleasant and 30% were very unpleasant.

   The regional consequences are those that impact the residential and suburban areas of Everett, Marysville, Tulalip and Lake Stevens. One of the over-arching observations about the regionalodor monitoring system is the influence of the local meteorology. There is a high percentage of calm conditions, wind speeds less than 1 m/s. For the meteorological data collected at the Cedar Grove and Marysville stations, thecalmhoursoccur 63.7 and 54.5%, respectively. Of all the observations, 94% occurred when the odor observers reported the associated wind speed as calm. Having a high percentage of calm conditions means that wind directions are likely to be quite variable, moving through several wind sectors in a short period of time. Wind speeds and directions are also likely to vary greatly from one location to another, since there is no overall windfield to drive local conditions.

   Predicting odor impacts using a dispersion model like AERMOD is also difficult when wind speeds are calm. Most dispersion models assume that the odor plume is moving downwind at a steady speed. This may not be true when wind speeds are light and wind directions are variable.

   Having two meteorological stations was helpful in defining some of the variations in wind speed and direction in the study area. Still these two stations could not characterize all the local conditions that would have existed from Marysville and Northeast Everett to Lake Stevens. In a typical OdoWatch system, one meteorological station would be provided for each site.

   Observations made by the odor observer committee are confirmed by the location of odor observations with respect to the locations of the plumes predictions from the regional odor monitoring system, and types of odor characteristic reported :

  • 36% of the observations occurred when an odor plume was at the location of the odor observation.
  • 24% of the odor observations occurred when an odor plume was predicted in the area of the observation and could have transited the location under light variable wind conditions.
  • 26% of the time, the predicted plume was in the direction of the odor observation, but did not extend to the location that the observation was made.
  • For 82% of the observations reported in Everett and 36% of the observations reported in Marysville, the OdoWatch predicted a plume reaching that location either during the same timeframe or a transient plume.

   Despite the limitations of making dispersion modeling predictions under calm conditions, the confirmation of odor observations with modeling results suggests that both the plume modeling and the odor observations are credible. Evenwith the limitations associated with modeling calm conditions, dispersion modeling remains the best approach to characterize the relationship between odor emission source and ambient odor impact. It defines the magnitude of the impact and frequency of exceedance, two key characteristics of odor.

Conclusions

   The findings of the study are summarized below:

  • Odor is multifaceted and impacts need to be described with respect to the frequency, intensity, duration and character. By combining source monitoring, odor modeling, and community odor observations, this study was able to define each of these qualities. Source monitoring and modeling defined the frequency and intensity of odor impacts. The community odor observations defined the characteristics and duration of odor events.
  • The meteorological data from the two stations, Cedar Grove and Marysville, show a high percentage of calm conditions (wind speeds less than 1 meter per second), 64 and 55 percent, respectively. This greatly affects how odor emissions disperse throughout the Snohomish River valley.
  • Dispersion modeling remains the best approach to characterize the relationship between odor emission source and ambient odor impact. It defines the magnitude of the impact and frequency of exceedance, two key characteristics of odor.
  • The residential and suburban areas of Everett, Marysville and Tulalip area are impacted by 3 facilities out of the 15 : the compost facility and the 2 Wastewater Treatment Plants.
  • Other sources characterized in the study did have odor plume profiles that extended beyond the plant property boundary. These impacts may be noticeable along roadways or public areas, but the impact in residential areas is limited.
  • The observations from the community odor committee provided valued input to the character of odors in the study area and confirmed the impacts from the regional odor monitoring system.
  • Sources that agreed to install eNoses at their facilities had the opportunity to access the OdoWatch system for their sites and monitor the odor emissions and impacts. Sources that agreed to have samples collected from their sites, gained knowledge about the odor concentrations from their operations.

References

40 CFR 51, Appendix W (2005) Code of FederalRegulations, Title 40 Part 51 Appendix W, “Guideline on Air Quality Models”. FederalRegister, Volume 70, No. 216, pgs 68218 – 68261, November 9, 2005

CEN - Committee for EuropeanNormalization. 2003. Air Quality – Determination of odour concentration by dynamicolfactometry; EN13725:2003. Brussels, Belgium.

U.S. EPA (2004), AERMOD: Description of Model Formulation, Office of Air Quality Planning and Standards, EPA-454/R-03-004, Research Triangle Park, NC, September 2004

U.S. EPA (2003), Comparison of Regulatory Design Concentrations, Office of Air Quality Planning and Standards, EPA-454/R-03-002, Research Triangle Park, NC, June 2003

 

 

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