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Published on: July 19, 2018
Odour concentrations prediction based on odorants concentrations from biosolid emissions
Radosław J Barczak1, Jakub Możaryn2, Ruth M Fisher3
1Faculty of Chemistry, University of Warsaw, 1 Pasteura Street, 02-093, Warsaw, Poland; UNSW Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, Australia; Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Poland.
This study models odorant concentrations in biosolids emissions to predict odor concentration (COD). Probabilistic methods accurately linked specific odorants to COD, improving monitoring in wastewater treatment plants (WWTPs).
Area of Science:
- Environmental Science
- Analytical Chemistry
- Wastewater Treatment
Background:
- Biosolids storage areas are major sources of odor emissions from wastewater treatment plants (WWTPs), impacting surrounding communities.
- Current odor impact regulations rely on odor concentration (COD) measurements from dynamic olfactometry, which can be subjective.
- Identifying specific odorants and their relationship to COD is crucial for developing objective, analytical monitoring techniques.
Purpose of the Study:
- To investigate the relationship between specific odorant concentrations and odor concentration (COD) in biosolids emissions.
- To evaluate the effectiveness of analytical techniques for predicting COD in wastewater treatment plant emissions.
- To assess the usability of probabilistic modeling for understanding odor dynamics in biosolids.
Main Methods:
- Analysis of 56 biosolids samples from two WWTPs in Sydney, Australia.
- Utilized both analytical and sensorial methods, including olfactory detection port (ODP) and dynamic olfactometry.
- Applied Bayesian Model Averaging and Variable Selection with Bayesian Adaptive Sampling to model COD based on 25 odorants and ODP-detected odor events.
Main Results:
- Established a link between concentrations of specific odorants and measured COD in biosolids emissions.
- Demonstrated the effectiveness of probabilistic methods and nonlinear transformations in modeling odor concentrations.
- Confirmed the accuracy of predictive models even with a relatively small dataset.
Conclusions:
- Probabilistic modeling offers a viable approach for predicting odor concentration (COD) from analytical measurements of odorants in biosolids.
- This research supports the development of more objective and accurate methods for monitoring and regulating odor emissions from wastewater treatment plants.
- The findings highlight the potential for analytical techniques to replace or supplement sensorial methods in odor assessment.
