Enrichment factor, calculation

We have employed two multi-elemental techniques (INAA and ICP-AES) to determine sulphur, halogens and 14 other trace elements in urban summer rainfall. Quality control was assured using NBS reference materials. The overall accuracy and precision of these two methods makes possible the routine analysis of many environmentally important trace elements in acid rain related investigations. Enrichment factor calculations showed that several elements including S, Cu, Zn and Cr were abnormally enriched in the urban atmosphere. A comparison of three separate sites showed a strong gradient of metal deposition from the industrial to the outlaying areas. 

Table IV. Effect of Solvents on the Enrichment Factor Calculated from the Compositions of the Phases and the Values of a for the Individual Exchange Reactions"...

Marty and Saliot (1976) gave the following values for the enrichment factor calculated for a 0.44 mm thick surface microlayer about 20 for dissolved and particulate n-alkanes for the Mediterranean Sea and between 161 and 350 for coastal samples collected along the French coast. These results are. comparable with those of Wade and Quinn (1975) who reported, for ultrasurface samples from the North Atlantic, values of the enrichment factor varying from 1.1 to 26. Analyses of sea surface samples from the Mediterranean by Morris (1974) indicated that the films collected, yielding a concentration of 40—230 mg of organic matter per m, were composed of both natural lipids in low amounts (<5% total extract) and a complex mixture of pollutant hydrocarbons. 

Table 3 Enrichment factors calculated according to eqn [2] for metals in suspended particulate matter or fine sediment from estuarine and coastal environments...

Hilton, J., W. Davison U. Ochsenbein, 1985. A mathematical model for analysis of sediment core data implications for enrichment factor calculations and trace-metal transport mechanisms. Chem. Geol. 48 281-291. 

Figure 4 Pb profile in the Swiss peat bog of EGR [165]. (a) Pb enrichment factor calculated as the ratio of Pb/Sc in the peat normalized to background value, (b) Isotopic composition of Pb summarized as ° Pb/ ° Pb.

A common approach for identifying the enriched elements in a material is to calculate the enrichment factor (EF). The EF of an element, M, is obtained by comparing its concentration with that of a reference element, R, such as cerium or... 

Result Transcription factor binding sites for the ER-enriched regions (calculated with RegionMiner and CEAS Tables 7.2 and 7.3). 

To estimate the contribution of wind-blown dust of crustal origin to the fine aerosol concentration, elemental enrichment factors were calculated using the method of Macias et al. (20). The enrichment factor, EF., for an element i was calculated as follows ... 

Iron was chosen as the reference element because its major source is likely to be soil and it is measured with good accuracy and precision by FIXE. Crustal abundances were taken from Mason (21). Enrichment factors greater than 1 indicate an enrichment of that element relative to crustal abundances values less than 1 indicate a depletion. The results of this calculation are shown in Table 4. For this calculation it was assumed that ammonium and nitrate accounted for all aerosol nitrogen. It is seen that Si and Ca are near their crustal abundance, indicating a probable soil dust source. The low EF for Al is probably due to a systematic error in the Al measurement rather than a true depletion. Potassium, although present in small concentrations, is slightly enriched relative to crust. The other fine aerosol species, C, N, S, and Pb are enriched by factors of thousands over their natural crustal abundance, indicating that they are not due to wind-blown dust. 

Table 9.11 shows the aerodynamic mass median diameter (MMD) for some typical inorganics that are common components of tropospheric particles. Also shown are the calculated crystal enrichment factors, EFcrusl. These are a measure of the enrichment of the element in the airborne particles compared to that expected for the earth s crust, using aluminum as the reference element. Thus EF,.rust for a particular element X is defined as... 

From Milford and Davidson (1985). b Calculated crustal enrichment factors. 

Whenever possible, amodel selection should be optimized before starting selections with a library. In this experiment, a mixture of active and inactive phage-enzymes is used as a model library for one round of selection. The phage mixture is analyzed before and after selection, yielding numbers that serve for the calculation of an enrichment factor (EF) ... 

Figure 4.7 Concentration polarization modulus ciolcih as a function of the Peclet number Jv8/Di for a range of values of the intrinsic enrichment factor E . Lines calculated through Equation (4.9). This figure shows that components that are enriched by the membrane (E0 > 1) are affected more by concentration polarization than components that are rejected by the membrane (E0 < 1) [13]...

Table 4.1 shows typical enrichments and calculated Peclet numbers for membrane processes with liquid feeds. In this table it is important to recognize the difference between enrichment and separation factor. The enrichments shown are calculated for the minor component. For example, in the dehydration of ethanol, a typical feed solution of 96 % ethanol and 4 % water yields a permeate containing about 80 % water the enrichment, that is, the ratio of the permeate to feed concentration, is about 20. In Figure 4.11, the calculated Peclet numbers and enrichments shown in Table 4.1 are plotted on the Wijmans graph to show the relative importance of concentration polarization for the processes listed. 

In rivers and streams heavy metals are distributed between the water, colloidal material, suspended matter, and the sedimented phases. The assessment of the mechanisms of deposition and remobilization of heavy metals into and from the sediment is one task for research on the behavior of metals in river systems [IRGOLIC and MARTELL, 1985]. It was hitherto, usual to calculate enrichment factors, for instance the geoaccumulation index for sediments [MULLER, 1979 1981], to compare the properties of elements. Distribution coefficients of the metal in water and in sediment fractions were calculated for some rivers to find general aspects of the enrichment behavior of metals [FOR-STNER and MULLER, 1974]. In-situ analyses or laboratory experiments with natural material in combination with speciation techniques are another means of investigation [LANDNER, 1987 CALMANO et al., 1992], Such experiments manifest univariate dependencies for the metals and other components, for instance between different metals and nitrilotriacetic acid [FORSTNER and SALOMONS, 1991], but the interactions in natural systems are often more complex. 

We can attempt to apply the same type of model to the H2S data, however there are two additional unknown factors involved. First, we do not have a measurement of the sea surface concentrations of H2S. Second, the piston velocity of H2S is enhanced by a chemical enrichment factor which, in laboratory studies, increases the transfer rate over that expected for the unionized species alone. Balls and Liss (5Q) demonstrated that at seawater pH the HS- present in solution contributes significantly to the total transport of H S across the interface. Since the degree of enrichment is not known under field conditions, we have assumed (as an upper limit) that the transfer occurs as if all of the labile sulfide (including HS ana weakly complexed sulfide) was present as H2S. In this case, the piston velocity of H2S would be the same as that of Radon for a given wind velocity, with a small correction (a factor of 1.14) for the estimated diffusivity difference. If we then specify the piston velocity and OH concentration we could calculate the concentration of H2S in the surface waters. Using the input conditions from model run B from Figure 4a (OH = 5 x 106 molecules/cm3, Vd = 3.1 m/day) yields a sea surface sulfide concentration of approximately 0.1 nM. Figure S illustrates the diurnal profile of atmospheric H2S which results from these calculations. 

Particulate emissions data for 21 studies of coal-fired power plants were compiled for use in receptor models. Enrichment factors were calculated (relative to Al) with respect to the earth s crust (EFcrust) and to the input coal (EFcoai). Enrichment factors for input coals relative to crustal material were also calculated. Enrichment factors for some elements that are most useful as tracers of coal emissions (e.g., As, Se) vary by more than ten-fold. The variability can be reduced by considering only the types of plants used in a given area, e.g., plants with electrostatic precipitators (ESPs) burning bituminous coal. For many elements (e.g., S, Se, As, V), EFcrust values are higher for plants with scrubbers than for plants with ESPs. For most lithophiles, EFcrust values are similar for the coarse (>2.5 ym) and fine (<2.5 ym) particle fractions. 

We also calculated "enrichment factors" to simplify comparisons between different plants. About half of the particulate matter in the atmosphere is suspended soil, so we have used enrichment factors with respect to the earth s crust to help identify potential coal tracer elements ... 

Enrichment factors (EF) were computed using aluminum and sodium as reference eelements for the earth1 s crust (4) and seawater ( 5), respectively. Enrichments were calculated by... 

Fig. 6.4. Yield vs. purity in cutoff selection. For the distributions represented in Fig. 6.3, retention probability (solid line) and enrichment factor (dotted line) were calculated as a function of the cutoff fluorescence value. For example, a cutoff value of 1.4 gives 95 % probability of retention of a given mutant cell, but a fairly modest enrichment factor of 11.8-fold. A cutoffof 3.0 increases the enrichment factor two orders of magnitude to 1000 x, at the cost of an increase in probability of clone loss to 50%.

Fig. 6.5. Peak spreading strongly affects enrichment ratio at fixed probability of retention. The coefficient of variance CV is equal to the ratio of the standard deviation to the mean, and is a measure of peak breadth. For example, in both curves shown in Fig. 6.3 the CV is 1.0. The enrichment ratio was calculated for a situation in which mutant fluorescence intensity was double wild-type fluorescence intensity, the mutant was initially present at 1 in 106 cells, and the probability of retention was fixed at 95 %. The logarithmic fluorescence intensity was assumed to follow a Gaussian distribution. Fixing the probability of retention defines the cutoff fluorescence value for screening at a given CV. Enrichment ratio drops precipitously with increasing CV, as the mutant and wild-type fluorescence distributions begin to overlap. At a CV of 0.2, the enrichment factor is 600. However, at a CV of 0.4, the enrichment factor has dropped to 3 Clearly, every effort should be expended to minimize peak spreading and subsequent overlap of the mutant and wild-type fluorescence distributions.

In practice this relation is only an approximation because of the uncertainty of all of the parameters. Nevertheless it is still an useful estimation. For example CS2 m water should be measured. The ZnSe-IRE with a length of 50 mm, a thickness of 3 mm, and windows with an angle of 45 °, allow 12 reflections in the sample area. It is coated with a PDMS membrane (n w 1.4) of 20 Xm thickness. The band at 1521 cm (= 6.575 pm) with a peak absorption coefficient of 3100 L mol cm is evaluated. With Eq. 6.5-1 and 6.5-3 the pathlength is calculated to be (V 0.31 A = 24.5 pm. Within this spectral range the noise amplitude is measured as 0.001 absorbance units. The enrichment factor/yv//w is 66. 

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