WATER I NNOVATE Pascal Harper Product Manager

2 Application Areas for Greenfield Sites –Incorporate odour emissions into process selection. Process Upgrades –Model comparisons between current and upgrade scenarios. –Provide variable emission rate data for OCU design (peak to mean ratios). Odour Management Planning –Diurnal profiling of odour emissions. –Changes to process operation influencing odours (liquor return rates/timings).

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4 Odour modelling currently: Neglects formation and emission Doesn’t consider perception Focuses on dispersion using: –emission rate estimation simple measurements, lit. data, educated guesses –steady state values ER almost always treated as a constant in modelling Background

5 Emission rates are driven by: –High mass transfer coefficient K L (wind/flow turbulence) –High surface area A –High odorant concentration in liquid phase C L (quality) Emission rate variations can be significant Importance of Emission Rates

6 Analytical metric required for: –Modelling formation / transformation in the liquid phase –Modelling mass transfer from liquid to atmosphere –Modelling dispersion in the atmosphere Sensory metric required for: –Modelling human perception & likeliness of complaint ODOURsim ® uses H 2 S as proxy for odour –Often the dominant odorant in wastewater –Easy to measure –Formation / transformation / mass transfers models available –Correlates with sensory measurements Choice of Metric

7 ODOUR Correlation - Primary Treatment Processes

8 Describes H 2 S behaviour in liquid phase Based on Hvitved-Jacobsen sewer model –Biofilms and suspended biomass –Considers anaerobic - aerobic conditions –Useful for H 2 S formation Modified by: –Adding mechanism for H 2 S to SO 4 2- oxidation Describes movement of H 2 S & O 2 into and out of liquid phase Based on mass transfer models for VOCs & O 2 in the literature –quiescent surfaces, weirs/drop structures, channels, dissolved air aeration, surface aeration, trickling filters Liquid Phase Formation Model Mass Transfer Emission Model Model Basics

9 - Model Application Applied to hypothetical situation: –10 km sewer Flows part-full at flows < daily average (Aerobic) Flows full at flows > daily average (Anaerobic) –Feeds simple primary treatment only STW Studies: –Effects of wind-driven PST emissions –Effects of flow & quality driven emissions

10 Wind blowing over liquid surface drives emissions –Induces turbulence (and increases surface area) through formation of waves Model simulation: –Used constant sulphide concentration in PST –Calculate emission rate vs. wind speed for 1 years Met. Data –Apply results to dispersion model: Using constant emission rate (the average for year) Using variable emission rate linked to wind speed avg - Wind-Driven PST Emissions

11 Demonstrates importance of linking emission rate to wind speed for liquid surface emissions. The constant (average) ER over-predicts odour footprint: ER value too high at low wind speeds when dispersal poor ER value too low at high wind speeds when dispersal good - Wind-Driven PST Emissions

12 Diurnal variations in influent wastewater drives emissions –High flows induce turbulence over weirs –High sulphide concentrations in liquid phase due to anaerobic conditions in the sewer Model simulation: –Use constant wind speed over PSTs –Calculate emission rate vs diurnal flow and load over 24 hrs –Observe: peaks in surface emissions due to elevated sulphide levels peaks in weir section emissions due to high flows –Apply results to dispersion model using range emission rates calculated - Flow and Quality Driven Emissions

13 Demonstrates the importance of linking emission rate to flow and quality parameters for weir and surface emissions. There is a potential for large error in dispersion model predictions if single spot ER measurements used - Flow & Quality Driven Emissions

14 Outcomes Emission rate variability is real Emission rate variability is driven by flow, quality, wind speed and temperature effects Emission rate variations have a significant impact on dispersion model predictions Emission rate variations should be included in dispersion model predictions –Percentile predictions have limited meaning if significant source of variability is ignored

15 ODOURsim ® - Data Requirements

16 Case study Sampled Wastewater characteristics –TCOD, SCOD, SH 2 S, pH, Temperature Micrometeorological Study carried out –Measured H 2 S concentrations on site –Measured wind velocity and direction –AERMOD model used to fit emissions Emission rates calculated by ODOURsim ® used as hourly emission file in AERMOD

17 - Emission variation over 48 hrs

18 ODOURsim UKWIR Micromet - Calibration Comparison

19 ODOURsim UKWIR Micromet - Validation Comparison

20 Static emission rates ODOURsim® variable emission rates 98 percentile contour plots