Odour calculation

The word butter comes from the Greek butyros. Butanoic acid (common name butyric acid) gives rancid butter its distinctive odour. Calculate the pH of a 1.0 x 10 mol/L solution of butanoic acid... 

At room temperature, trimethylamine, (CHsjsN, is a gas with a strong ammonia-like odour. Calculate [OH ] and the percent of trimethylamine molecules that react with water in a 0.22 mol/L aqueous solution. 

CCl3CH(OH)2, C3H3CI3O2. Crystallizes in large, colourless prisms having a peculiar odour m.p. 57"C, b.p. 91-5 C. Manufactured by adding the calculated amount of water to chloral. For other properties see chloral. Its chief use is as a hypnotic. 

To compare between days and locations I standardised visitation rates for each species by calculating visits to each odour type as a proportion of mean visits to the blank stations by that species, on that night, and at that location. By making the data proportional I removed effects of differing population size and density, as well as variation between nights, for example in moon phase. These data were checked for normality and symmetry, and subsequently analysed with a two-tailed Wilcoxon signed ranks test (Zar 1999) to determine whether visitation rate differed from a value of one (indicating no difference between visitation rate to an odour source... 

As it was known from other experiments that the NH3 losses per ton of compost produced were 2, 1 kg, he could calculate the emission in odour unit/s from this composting plant. 

To be sure the panel receives the same dilution as can been calculated from the flow rates the odourless air should be odourless and adsorbtion of odours should be prevented or minimised. In the French proposal odourless is defined as follows a gas which every panel member invariably describes as odourless. This is air or nitrogen passed through an active carbon filter. 

Guideline VDI 3781 part 5 completes the complex of odour determination and judgement with the calculation of dispersion models. The calculation methode and odour determination by panelists should give comparable results. 

Some experiences with olfactometric measurements in connection with odour abatement processes, mainly in sewage sludge and waste water treatment plants and in the fish meal industry, are presented. Studies have been carried out to calculate the efficiency of various odour reducing Methods. The additional information provided by the measurements was of practical use for the management of the process to improve odour reducing efficiency. 

The olfactometer supplies 6 dilution levels. At each dilution level 3 samples ( triangle ) are presented to the panelist from a set of glass sniffing ports, two presenting the test room air (blanks). The third is the odorous gas sample diluted with test room air. The panelist is instructed that one of the three ports at each dilution level exhibits an odour, and that his task is to smell the effluents from the ports and decide which port, in his opinion, delivers an odorous sample. If the panelist can smell no difference he has to guess ( forced triangle ). This is included in the statistical basis for calculation of the total odour strength. 

Samples for the olfactometric measurements were taken in different positions in the installations and the odour reducing efficiency was calculated as the ratio between the recorded ED50 value at the outlet and inlet of the purification steps ... 

On the other hand, the set of data turned out to be a useful tool for calculating the limitations of each odour reducing method under definite conditions. The results also allowed precautions for appropriate process management under conditions causing high levels of odorous compounds. 

The last set of requirements in olfactometry is concerned with the differences between panel members. People vary widely in their sensitivity. A factor of a 100 between the thresholds of two subjects for the same substance is not uncommon. For a number of substances, specific anosmia s or specific hyposmia s are found. In such cases a person has no sensitivity at all or a very high threshold for the given substance, but normal sensitivity to other substances (1). This is an illustration of the fact that sensitivity to odours is specific rather than general. This is also demonstrated by Punter (2, 3) who determined the thresholds of 69 odorous substances for the same group of subjects and calculated the correlations between these thresholds (see figure 2). 

The aim of dispersion models is to develop reliable methods for calculating the atmospheric dilution of airborne pollutants under practical conditions. One application in agriculture is the determination of that distance, at which i.g. odouriferous pollutants of an animal farm are diluted in the atmosphere to a concentration below a certain threshold, in order to allow the farmer a profitable production and likewise to prevent odour nuisance from the neighbourhood. 

Despite the technical advances in the past decade, no apparatus for measurement of the odour strength has been developed. Therefore, odour pollution studies cannot be performed without using human noses. In general, the efect of polluting odours can be studied either by direct assessment in the ambient air or by means of a dispersion calculation. The first method requires a number of observers to be placed in the vincinity of the odour source (3,7). The latter a dispersion model and an input value. For reasons of simplicity this method is most frequently used in the Netherlands. 

In case of an odour measurement the inhaled air is supposed to be equal to the air delivered by the olfactometer. However if flowrates of the olfactometer are not equal to the respiration level, additional air from the experimental room will be used (see figure 2). Since it is a good practice to keep an experimental room free from odour contamination the additional air can be considered as diluting air. In that case the final dilution of the odour sample is higher than calculated. The result may be negative response in cases where a positive response should occur. An increase of the olfactometer flowrate will replace unaccounted diluting air by accounted air thus making the final dilution is closer to the desired dilution. 

The results from (i) are shown in Table II which records odour potential for sludge collected each Thursday morning over a 4 month period. Results for (ii) and (iii) are shown in Table III, which records the range of odour potential for samples collected over a one year period. Mean values for odour potential are also shown, these having been calculated from approximately 15 measurements. 

On the basis of these responses a weekly Odour Annoyance Index was calculated for each of the four locations. This index (described elsewhere in detail (2) ranges from 0 to 100, if nobody smells anything the index has the value 0, if everybody smells a extremely annoying odour it has the value 100. 

In table 1 some results from this study (2) of odour reduction are presented. The thresholds are mean values expressed as log dilution factors. Standard deviations are calculated on the averages of doubled ED-50 values, each value based on the reports of six observers at a time. As can be seen, incorporation of the manure largely reduced the emission of odour from the field. This mainly concerns the injection techniques, which in some cases reduced the odour to the background level. Conventional tillage implements such as a plow or a disc harrow also reduced the odour emission considerably. 

The investigations of dust from piggeries show that both VFA and phenols/indoles are present in a considerable amount. However, compared to the air-borne emissions calculated on the base of the results of LOGTENBERG and STORK (38) less than the tenth part (1/10) of phenols/indoles and about the hundredth part (1/100) of VFA are emitted by the dust, only. Table VII compares the dust-borne and air-borne emissions of VFA and phenols from piggeries. The total amounts are given in addition to the amounts of butyric acid and p-cresol which are both known as intensively smelling compounds. The recognition odour threshold values of these two components are included, as well. Under the assumption of a dust concentration of 10 mg/m3 (7) one cubic meter of air... 

If there is a need for odour reduction on an animal husbandry farm anaerobic digestion is one of the real possibilities. The investment and running costs of the methods, mentioned in table 3, are indicated in previous chapters. An economic calculation for the feasibility of an anaerobic digestion is open for discussion. For our purpose we must valuate the nett output of biogas. 

Table 1 showed that for a 500-pig unit the annual losses are about Dfl. 7.000,-. These losses are calculated (7) under optimal conditions. When odour reduction is the first goal, losses can be higher. Suppose that 50% of the produced biogas is not used. The extra losses are 5000 m3 a Dfl. 0,52=Dfl. 2.600,- higher. So total losses increase to Dfl. 9.600,-. Per pigplace are the costs for odour reduction nearly Dfl. 20,-. 

However, although this equation was effective in modelling the odour thresholds of the disubstituted pyrazines, two main weaknesses have been identified (72) the first was that it was difficult to dmw physical meaning from the descriptor AA J, since it was not clear which aspects of die molecular structure determined the odour threshold. The second we ess was discovered when pyrazine itself and thirteen mono-substituted pyrazines were added to the original set. The calculated and observed odour threshold values were no longer in agreement. This result indicated diat the model was insufficient for more heterogeneous data sets. 

With the molecular descriptors as the X-block, and the senso scores for sweet as the Y-block, PLS was used to calculate a predictive model using the Unscrambler program version 3.1 (CAMO A/S, Jarleveien 4, N-7041 Trondheim, Norway). When the full set of 17 phenols was us, optimal prediction of sweet odour was shown with 1 factor. Loadings of variables and scores of compounds on the first two factors are shown in Fig es 1 and 2 respectively. Figure 3 shows predicted sweet odour score plotted against that provid by the sensory panel. Vanillin, with a sensory score of 3.3, was an obvious outlier in this set, and so the model was recalculated without it. Again 1 factor was r uired for optimal prediction, shown in Figure 4. 

OAVs are calculated on the basis of odour threshold values which have been estimated in a medium that predominates in the food, e.g. water, oil or starch. As an example, the OAVs of the odorants of pineapples are listed in Table 16.7. 

Odour activity values were calculated by dividing the concentrations of the odorants by their orthonasal odour thresholds in water... 

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