Odour Quantification & Dispersion Modelling - Report Example

Manoo R Name Lecturer Course Date Part 1 Olfactometry is a method of measuring odours in the environment. It uses odour unit and concentration to determine its effects on human beings. There are a number of variances that are likely to occur when olfactometry data is being analysed. These variances include; Specific panel variance, within panel variance, Inter-laboratory variance and Inter-panel variance. Inter-laboratory variance is the variable between different laboratories that has been used to test odour of the livestock. It is usually considered as accurate if the laboratory is from the same area. Specific panel variance is the variable between different researchers from the same test session and it is accurate and specific to a certain temperature and environment but cannot be relied upon as a threshold result. Inter-panel variance is a product from a single experiment in a laboratory but carried out at different times using different panels. These variances are from results that used n-butanol ‘as a standard test odorant’ and regular basis. Lastly, within panel variance is the variable obtained from repeated analysis in a laboratory and using one panel. Odour unit is 1 o.u./m3 which is a measurement of odour which i.e when odour is perceived by 50% of a panel. It s done when panel of observers are made to find out the level of odour. While odour concentration is number of dilutions with oxygen, it is determined by using odour-free air to dilute the sample to a level where 50% of a panel of people smelling the odour can just detect it (Blyth, 2003; Cudmore, Pacific, Ryan and and Merz, 2002.). From the results it shows that the pattern of the plotted points on the graph slopes from upper left of the scatter plot to lower right suggesting a negative relationship between the two variables. This kind of association simply implies that as the concentration decreases as the distance increases (Powell, 2006; Zhang, Feddes, Edeogu and Zhou, 2002). This kind of findings simply goes with the general expectation. For a normal odour it is generally hypothesized that it would have adverse effect on demand. The relationship between odour concentration (OUE/m3) and a 8-point n-butanol intensity scale for swine odour is predicted using Weber-Fechner model. The two parameters were determined to be k1 = 0.82 and k2 = 0.36. It should be noted that these two values were valid only for the 8-point n-butanol intensity scale. To compare the current model with others, the intensity levels were converted to equivalent n-butanol concentration. The relationship between the intensity level and the n-butanol concentration was approximately logarithmic (Freeman, T., Steven, B & Freeman Environmental Solutions Ltd, 2010; Nicolai, Clanton and Guo, 2000). The general nature of the equation is: D = S × √N (1) Where D = separation distance S = composite site factor (S1 × S2 × S3 × … × Sn) N = number of standard animal units. D = 0.78 × √10 = 2.5km From the results of the study which was 5km, we find that the concentration has a correlation of 0.05. This shows that the intensity of odour decreases as a distance from the source increases (AAFRDTechnical Services Division Office, 2002). The slope of the curve shows that there is persistence of odour but as the distance increases, it reduces. The intensity of odour was measured using 8-point n-butanol intensity scale which predicts intensity. However, odour concentration was done by measurements of olfameters (Zhao, Darr, Wang, Manuzon, Brugger, Imerman, Arnold, Keener and Heber, 2007; Hong, Lee, Hwang, Seo, Kwon, Bitog, Yoo, Kwon, Ha, and Kim 2008). It should be noted that odour is usually diluted when it is moving in the atmosphere does its intensity decreases. A more persistent odour will have a negative impact to human health. The strength of emission from the source will be determined using a regression equation of y = -0.12x + 3.168. Conclusion The aim of Gaussian is find a solution for a complex problem with modelling. In other words odour problem will solved through calculations with modelling, this will be very tedious. It estimates values of variable and will require programming. It is important because it’s used to sturdy other models and it cannot be used to study odour problem. The advantage of is that it is to reduce modelling impact and considers unsteady elements(Piringer and Schauberger, 1999). One of the disadvantages is that it uses 3D simulations and sub-grid models. it also requires numerical calculations and the use known data making it academic friendly(Chastain and Wolak, 1999). The zone model approach has been found to be the most commonly used as it is less demanding when computing and also is available at a lower cost. On the other hand it gives primary technique behind model in which involves large tabulation of data that carries the bulk of the energy system and require direct resolutions so as to represent a flow process that has the desired accuracy (Hong, Lee, Seo, Bitog and Kwon, 2012). Part 2 INPUT 1. EFFLUENT CHARACTERISTICS Discharge (m^3/s): 0.6 -----> 0.6 (m^3/s) CBOD5 (mg/L): 130 Ultimate BOD (mg/L): 0 -----> 0 Dissolved Oxygen (mg/L): 0.5 Temperature (deg C): 27 2. RECEIVING WATER CHARACTERISTICS Discharge (m^3/s): 4.8 CBOD5 (mg/L): 0.0 Ultimate BOD (mg/L): 5 -----> 5 Dissolved Oxygen (mg/L): 7.4 Temperature (deg C): 22 Elevation (feet above sea level):   -----> 0.0 m a.s.l. Downstream Average Channel Slope (m/m):   River Average Depth (m): 1.2 River Average Velocity (m/s): 0.1 3. REOXYGENATION RATE (Base 10) AT 20 deg C (day^-1): 0.33 4. DEOXYGENATION RATE (Base 10) AT 20 deg C (day^-1): 0.33 0.33         OUTPUT 1. INITIAL MIXED RIVER CONDITION Initial discharge (upstream + effuent discharge) 5.40 CBOD5 (mg/L): 14.44 NBOD (mg/L): 4.44 Dissolved Oxygen (mg/L): 6.6 Temperature (deg C): 22.6 2. TEMPERATURE ADJUSTED RATE CONSTANTS (Base 10) Reaeration (day^-1): 0.34 BOD Decay (day^-1): 0.37 3. CALCULATED INITIAL ULTIMATE CBODU AND TOTAL BODU Initial Mixed CBODU (mg/L): 21.2 Initial Mixed Total BODU (CBODu + NBOD, mg/L): 25.7 4. INITIAL DISSOLVED OXYGEN DEFICIT Saturation Dissolved Oxygen (mg/L): 8.79 Initial Deficit (mg/L): 2.16 5. TRAVEL TIME TO CRITICAL DO CONCENTRATION (days): 1.12 6. DISTANCE TO CRITICAL DO CONCENTRATION (km): 9.66 7. CRITICAL DO DEFICIT (mg/L): 10.67 8. CRITICAL DO CONCENTRATION (mg/L): -1.88         A). 1). Expected critical DO concentration is Where Qw = volumetric flow rate of wastewater, m3/s Qr = volumetric flow rate of river, m3/s DOw = dissolved oxygen concentration in the wastewater, g/m3 DOr = dissolved oxygen concentration in the river, g/m3 Lw = ultimate BOD of the wastewater, mg/L Lr = ultimate BOD of the river, mg/L La = initial ultimate BOD after mixing mg/L Sag Equation (Weber‑Shirk, and Dick,1997). dD/dt = repsents the change in oxygen deficit (D) per unit time, mg/L.d kd = deoxygenation rate constant, d-1 kr = reaeration rate constant, d-1 L = ultimate BOD of river water, mg/L D = oxygen deficit in river water, mg/L Critical DO concentration -1.88 mg/L 2). The expected distance of the critical location is 9.66km 3). Discharge rate of the waste water to maintain at least 5mg/L Critical concentration (Weber‑Shirk, and Dick,1997). Where Qw = volumetric flow rate of wastewater, m3/s Qr = volumetric flow rate of river, m3/s DOw = dissolved oxygen concentration in the wastewater, g/m3 DOr = dissolved oxygen concentration in the river, g/m3 Lw = ultimate BOD of the wastewater, mg/L Lr = ultimate BOD of the river, mg/L La = initial ultimate BOD after mixing mg/L (American Water Association, 2002). 5mg/L = = 32m3/s B). Retention time of the wastewater is calculated as Time = Velocity is determined by V= - (Vigneswaran and Dharmappa, 1993). Where V is velocity, n is soil porosity, K is hydraulic conductivity and dh/dx is hydraulic gradient K = 1.5mm/s, n=0.27 dh/dx ( hydraulic gradient) = (wl2 - wl1)/d where, wl1 = water level 1, wl2 = water level 2, d = Distance Between Wells [L] wl1 = 123.15, wl2 = 123.05, d = 30m dh/dx ( hydraulic gradient) = (123.05 – 123.15)/30 = -0.0033 thus V= - = 0.0185mm/s Time = = = 18.75 days C) NaOH(aq) +HNO3(aq)NaNO3(aq)+ H2O 40+63 85 m = c x V (Tugtas, 2012). Mass of nitric acid = 150mg/L x 730L/min = 112.5kg/min The ratio of nitric acid to sodium nitrate is NaOH HNO3 40 63 x 112.5 X112.5kg/min = 71.43kg/min or 102,857kg/d b) Estimation of the total dissolved solids after neutralisation If a finely ground substance is placed in water, one three things will happen. First, it may form a true solution that is simply a dispersion of atoms, molecules, or ions of the substance into a solvent. Particles in the solution do not exceed 1 nm in size. Second, the particles may remain larger than 100 nm. These particles are large enough to be seen with microscope and gradually fall to the bottom of the container. Because the particles are temporarily suspended and settle out upon standing, this mixture is called a suspension. (Tugtas, 2012). Total Dissolved Solids = 250mg/L+ (71.43kg/min)/730L = 347.85gm/L C).1. Order of Reaction Reaction time (min) Measured Concentration Reaction rate (mmol/L) order of reaction 0 4.2 4.2 1 3.645 3.645 0.86785714 2 3.18 1.59 0.43621399 5 2.085 0.417 0.26226415 10 1.035 0.1035 0.24820144 20 0.255 0.01275 0.12318841 The order of reaction is m=1 and n=2 b). Value of the k rate constant rate = k(mmol/L) (Vigneswaran and Dharmappa, 1993). k = rate constant t = time 0 = original conc mmol/L at t = 0 plotting log10 mmol/L vs time gives a straight line Using the equation of a line, y = a + bx y = log10 mmol/L x = time, t y intercept, a = log10(mmol/L)0 slope, b = -k slope, b = -0.06 thus k = 0.06 3. divide mg/L by 98 to get mmol/L = 1mg/L/98 = 0.0102 mmol/L Where A is 0.00102 mmol/L, k= 0.06, A0 = 4.2 mmol/L In(0.0102 mmol/L) = -0.06 x t+In 4.2 mmol/L 0.06 x t = In 4.2 mmol/L- In(0.0102 mmol/L) = 6.02 0.06 x t = 6.02 Time is 100.34 minutes References AAFRDTechnical Services Division Office, 2002. Dealing with Livestock Odour Concerns. Retrieved April, 2013, from < http://www1.agric.gov.ab.ca/$department/deptdocs.nsf/all/epw8447/$file/AFRDOdourFactsheet1.pdf?OpenElement> American Water Association. (2002). Simplified Procedures for Water Examination: Book and Elements Chart - Chlorine Residual and Chlorine Demand. New York: American Water Works Assn Blyth, L., (2003). Odour Quantification & Dispersion Modelling. IDS-Environment - White Paper. Retrieved April, 2013, from Chastain, J. P. & Wolak, F. J. (1999). Application of A Gaussian Plume Model of Odor Dispersion To Select A Site For Livestock Facilities. Retrieved April, 2013, from Cudmore,R., Pacific, A., Ryan, D. & Merz, S. K., 2002.Good Practice Guide for Odour Management in New Zealand. Workshop, 18th October 2002 CHRISTCHURCH. Retrieved April, 2013, from Freeman, T., Steven, B & Freeman Environmental Solutions Ltd, (2010). Analysis of Options for Odour Evaluation for Industrial or Trade Processes. Retrieved April, 2013, from Hong, S., I. Lee, H. Hwang, I. Seo, H. Kwon, J. Bitog, J. Yoo, K. Kwon, T. Ha, and Y. Kim (2008). Field experiment for developing an atmospheric diffusion model of a livestock odor. Journal of the KSAE 50(4): 77-88. Hong, S.W., Lee, I.B. Seo, I.H. Bitog, J.P. & Kwon,K.S. (2012). Prediction of Livestock Odour Dispersion over Complex Terrain using CFD Technology. Retrieved April, 2013, from Nicolai, R.E., C.J. Clanton and H. Guo. (2000). Modeling the relationship between detection threshold and intensity of swine odours. In Proceedings of the Second International Conference: Air Pollution from Agricultural Operations, 296-304. St. Joseph, MI: ASAE. Powell, J.M. (2006)., 'A review of research needs for dairy shed effluent management', Department of Primary Industries, Melbourne. Piringer, M. and G. Schauberger (1999). Comparison of a Gaussian diffusion model with guidelines for calculating the separation distance between livestock farming and residential areas to avoid odour annoyance. Atmospheric Environment. 33(1999): 2219-2228. Tugtas, A., (2012). Environmental Engineering Unit Operations-Sedimentation II. Marmara University. Vigneswaran, S. & Dharmappa H. B., (1993). Unpublished Notes on Wastes and Wastewater Treatment, University of Technology, Sydney. Weber‑Shirk, M., and R. I. Dick. (1997). Physical‑Chemical Mechanisms in Slow Sand Filters. Jour. AWWA. 89:87‑100. Zhang, Q., Feddes, J.J.R., Edeogu, I.K & Zhou, X.J., (2002). Correlation between odour intensity assessed by human assessors and odour concentration measured with olfactometers. Canadian Biosystems Engineering. Retrieved April, 2013, from Zhao, L.Y., Darr, M., Wang, X., Manuzon, R., Brugger,M., Imerman, E., Arnold, G., Keener, H. & Heber, A.J. ( 2007). 'Temporal variations in gas and odor emissions from a dairy manure storage pond', Paper No. 701P0507e, Proceedings of the Sixth International Dairy Housing Conference, Minneapolis, Minnesota, USA, 16-18 June 2007, ASABE. Read More