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galvis

Water treatment

were poured into a

were poured into a funnel using filter No. 595 Schleicher and Schül filter paper with 5-µm pore size. The filtered water volume was measured after 3 minutes of filtration. Faecal coliform analysis was performed following the membrane filter technique using Gelman sciences sterilised membrane filters with 0.45 µm pore size and lauryl sulphate broth. The incubation period was 16 ± 2 h at 44.5 ± 0.2ºC (APHA et al., 1989; Del Agua, 1989). Duplicate tests were performed from each sample and depending on the stage of treatment, the following sample volumes were used: 1 and 0.1 ml for raw water and effluents from DyGF units, 60 and 10 ml for effluents from CGF lines, and 100 ml for SSF effluents. Results are reported as colony forming units (CFU) per 100 ml of sample. The amount of sludge or silt stored in the SSF beds was estimated by performing silt tests. Sand samples were collected from the SSF units to perform this test. Around 50 ml of sand and 200 ml of water were poured into a transparent cylinder of 250 ml. The mixture was then agitated for 2 minutes. The suspension of water, sand and sludge was then allowed to settle during 24 h. The result is presented as the % of the volume of silt on top of the sand divided by the volume of sand. According to Lloyd (1974), washed sand should report silt test results of 1-2%, scraped SSF skin 20%, and filter sand that needs to be replaced >12%. 3.1.8 Data management An information system was designed consisting of coded sampling points (as identified in figure 3.8), labelled sampling containers, sampling or data collection formats, and a data base. This system was fully implemented for the physicochemical and bacteriological data, and partially for data related to hydraulic measurements, and O&M procedures. Database. A computerised database was initially programmed in Symphony, version 1.1, but later, during the writing of this thesis, it was adapted to excel 97 of Microsoft Office, under Windows 95. Database includes the possibility of file combinations to gather a set of crossrelated data in tables, figures or reports. A simplified version of the database is shown in figure 3.10. • Raw water Test Periods Water quality and hydraulic parameters • DyGF units • Integrated water • UGFS + SSF 1 • UGFL+ SSF 2 • MHGF + SSF 3 • HGF + SSF 4 • DGFS + SSF 5 Figure 3.10 Simplified scheme of database for water treatment studies with MSF pilot system at Cinara´s Research station in Puerto Mallarino, Cali, Colombia. 85

Data analysis. Data were analysed to obtain descriptive statistics and to investigate some statistical inferences aimed at comparing treatment stages working in parallel in the MSF pilot system. Statistics such as mean and median were used to describe central tendencies, and standard deviation, minimum and maximum values to describe dispersion. These statistics as well as frequency distributions are presented in tables and graphs. Statistical inference allows inductive processes for setting confidence bands or testing hypotheses based on sets of data (samples) produced during the experimental work. In these processes probability theory is used to estimate the significance of differences in the observed values as caused by treatment levels included in the experiments. To include the impact of raw water quality changes between sampling sections (blocks), the randomized block design was the statistical model used to describe the experimental work and to make comparative analyses in the present study (Finney, 1968; Vargas, 1991). The model includes the expressions, treatments and blocks, which need to be clarified in the context of the present study. Filtration rate is considered the treatment (τ i ) in applying the model to the 1 st filtration stage (DyGF), with 3 levels i. This means i = 3 considering the DyGF units (A, B, and C) running in parallel with different filtration rates (figure 3.8 and table 3.1). Coarse gravel filtration (CGF) alternatives are considered the treatment (τ i ) in applying the model to the 2 nd filtration stage (CGF) with 5 levels of i. This signifies i = 5 taking into account the CGF options (UGFS, UGFL, MHGF, HGF, and DGFS) included in this stage and running in parallel with similar filtration rates during each test period (figure 3.8 and table 3.1). Each block corresponds to each sampling session in which the same parameter is being monitored across all treatment levels. Water quality ranges can be used to take into account the raw water quality changes during the experimental periods. The statistical model used in this research is represented by the equation 3.1 Yij = µ + τi + βj + εij where (3.1) Y ij: Observed values in all i-treatment levels with j-observed values. µ: Overall mean of observed values (Y ij ) without distinguishing between treatment levels τ i : Fixed effect due to i-treatment level. In this study i takes values between 1 and 3 for the DyGF (1 st filtration stage) and between 1 and 5 for the CGF lines (2 nd filtration stage) working in series. β j : Random effect due to block j, meaning the j-observed values, with j changing from 1 to b, with b being the number of observed values for each treatment level. ε ij : Random variation associated with the i-treatment level in the j-block of observed values (the experimental error) The basic assumption (null hypothesis) used to apply this model is that observed values during test runs, divided among several groups according to treatment levels and sampling sessions, are all from the same population, meaning that there are not significant differences between the groups (samples) means. In the context of this study the null (H o ) and the alternate (H a ) hypotheses are as follows. 86