Aggregate parameters

A lot of substances and components are present in wastewaters and can be measured, especially the emerging pollutants. However, in practice, the aggregate parameters (BOD, COD, TSS,...) and the physico-chemical ones (temperature, pH, dissolved oxygen, conductivity, turbidity,...) are more often monitored. The only specific compounds generally considered are the N and P forms, and in case of industrial wastewaters, some specific pollutants such as organics (phenolics, hydrocarbons,...) or metallic compounds. 

Figure 9. Main approaches for aggregate parameters estimation from UV spectra exploitation.

The study of numerical results related to the regression study for each aggregated parameter comparison is not sufficient. On one hand, the distribution of the results must be carefully checked, for example, with the study of the residuals variation (Fig. 13), corresponding to a residual standard deviation (RSD) of 20%. In this case, no particular... 

More precisely, the main organic compound families are more or less recovered by the aggregate parameters and by UV spectrophotometry (Table 7). Additional comments can be made concerning organic compounds containing hetero-atoms (N or S). They are at... 

Surfactants are usually an interesting fingerprint of domestic pollution, responsible for the relatively high values of aggregate parameters (TOC, BOD, COD). In this case, a simpler visual characteristic can be used for pollution diagnosis, the presence of white persistent foam, particularly in aerated area, related to the presence of surfactants. 

Urban wastewater is most likely the best experimental field for the application of UV spectrophotometry. The first reason is that organic pollution is composed of unsaturated compounds, which enables finding, for any sample, simple relations between its UV spectrum and the value of aggregate parameters such as COD, BOD, etc. (see Chapter 4). As a consequence, many researches have been conducted on this topic for more than 30 years [1-3], All these authors have proposed the measurement of the absorbance at 254 nm. Even if this wavelength seems to correspond to a shoulder in the spectrum, the reason for the choice of this wavelength was more practical (254 nm being the main radiation below 300 nm of low-pressure mercury lamp), than scientific, because of possible interferences from industrial discharges. 

The relation between concentration and flow is clearly shown in this example. Indeed, flow rates and UV spectra present the same variation. During the night, the sample presents a spectrum characterised by a monotonous shape without any shoulder. This can be easily related to a low domestic activity. On the contrary, day time spectra present a more important pattern, with the shoulder associated to the presence of surfactants. A correlation between the values of flow rates and aggregate parameters plus some specific compounds (detergents) could easily be established. This example shows that the knowledge of time variation in wastewater quality is very important for sewer and treatment plant management. 

In Fig. 19 are presented the spectra of inlet and outlet of a large treatment plant characterised by a rather short mean residence time in the aeration basin (few hours). This process, working with a high-load F/M ratio, does not lead to the complete biodegradation of organic matter as it is shown with the outlet spectrum shape. The efficiency is quantified by removal yields calculated from different aggregate parameters (Table 3). 

In the case of industrial wastewater, several other compounds can also be involved, such as sulphide, hexavalent chromium or organic molecules. This last group of compounds is considered hereafter with regard to their economic and/or environmental importance. For example, Fig. 5 presents several specific compounds encountered in refinery and petrochemical wastewater. Thus, phenol, EPA (ethylpropylacrolein), TBC (tertiobutylcatechol), NMP (/V-methylpyrolidone) and nitrite can be detected in effluents or process water [21], Moreover, the estimation of complementary aggregate parameters, such as total oxygen demand (TOD), is possible from the estimation of one of the previous organic compounds [3] (Fig. 6). 

Table 1. Aggregate parameters of the studied landfill leachates...

Other aggregate parameters which can affect the performance of cement include surface texture, bulk density, particle density, drying shrinkage, thermal movement and fire resistance. The properties of hard, dense limestones are such that they are generally suitable for use in concrete [8.1]. 

Figure 15. Evolution of the cluster-mass distribution for different time moments corresponding to the aggregation parameter = 0.2-0.8(0.2), 0.9, 0.92-0.98(0.02)...

Figure 16. Evolution of the extinction and static light scattering spectra with time expressed in terms of the aggregation parameter /, = 0, 0.2(1), 0.4(2), 0.6(3), 0.8(4), 0.9(5), 0.98 (6)...

Table 5.18. Roughness parameters and curvature radii of nodule aggregates (parameters were calculated on a 2 X 2 pm area scan) on plasma-treated cellulose ester membranes...

The size distribution function of the clusters is a favourable tool to reveal statistical information of the aggregation process. The goal of our work was to find links between the cluster size distribution (CSD), its functionals and other aggregation parameters (aggregation area, initial surface coverage of the... 

In addition to real experiments, off-lattice molecular dynamics computer simulations [2] were run to get a deeper understanding of the experimental results. The values of the aggregation parameters used in the simulations are given in Table 1. 

Figure 1.4 The relation between aggregation parameter k(d) and cluster functionality F for eight amorphous and semi-crystalline polymers [30]...

The aggregation parameter k(d) value was estimated according to Equation 1.16 for eight polymers at T = 293 K. It is obvious that the physical significance of this value must be analogous to the functionality F for the cluster structure of the polymers. The relation between k(d) and F values is shown in Figure 1.4, from which the expected correspondence follows. It is assumed from this observation and the fact that k d) is independent of temperature that the polymer amorphous state k d) represents some qualitative measure of the polymer s ability to form clusters at the segmental level [30]. 

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