Sewage characterization and insewer transformation of selected determinants.

A comprehensive sewage characterization was conducted to further understand the nature of sewage generated in three different catchments: domestic, commercial and industrial areas. It was revealed that the characteristic sewage generated in domestic areas resembled the typical sewage characteristic reported globally. However, sewage generated in industrial areas indicated interferences or adulteration from industrial processing effluents. The heavy metal characterization revealed that most STPs treated effluents complied with Standard B of Environmental Quality Act (EQA) 1979 in term of Ni, Cu, Zn, Fe, Pb and Cr. The assessment of STPs performance grouped according to their locations discovered that STPs which served population exceeded the design population equivalent (PE) demonstrated poor performance due to overloading factor. The lower efficiency of COD, BOD5 and SS removal of STPs located in industrial areas might be contributed to industrial processing effluents contamination. It was reported that 86.1% of BOD5, 71.46% of COD and 95.9% of SS of cumulative probabilities of treated effluent from STPs in domestic areas met the permissible limit in the Standard B of EQA. The cumulative probability of BOD5, COD and SS of treated effluents of STPs in commercial areas to meet the Standard B were 95.4%, 80.0% and 97, 95% accordingly. The percentage of cumulative probability for the treated effluents of STPs in industrial areas portrayed lower values which were 68.61% (BOD5), 56.0% (COD) and 77.3% (SS) respectively. The screening of Persistent Organic Pollutants (POPs) in treated effluent of selected STPs revealed the presence of eight pollutants; phenol, naphthalene, bis(2-chloroisopropyl) ether, dimethyl phthalate, diethyl phthalate, ethyl benzene, 1, 4-dichlorobenzene and phenanthrene. The range of concentrations varied with phenol depicted the most abundance compound detected in all studied STPs’ effluents In the determination of reaction rate and order, it was found that all samples followed first order reaction and the rate, K varied from 0.00192 to 0.00336 L/mgMLVSS/day. The decay coefficient, b, obtained ranged from 0.167 to 0.3360 day-1. The fitting of Monod and Haldane models produced sets of biokinetic constants. The fractionation study revealed that only 61% to 69% of total COD were truly biodegradable in nature. Meanwhile, the fraction of readily biodegradable ranged from 15% to 38%, the non-biodegradable ranged from 31% to 39% and the slowly biodegradable ranged from 23% to 44%. The in situ characterization of sewerage network concluded that variation in the parameters analyzed between the sampling points. The course of degradation of COD, SCOD, phenol, anionic surfactants and sulphide were followed in aerobic and anaerobic conditions mimicked the in sewer environment. In aerobic condition, the zero-order rate of COD and SCOD were 0.051mg/l/hr and 0.052 mg/l/hour respectively. In anaerobic condition, it was observed that there was negligible COD removal. However, SCOD concentration was observed to increase with incubation time with the rate of 0.010 mg/l/hr. Phenol concentration was observed to decrease with the rate of removal 0.021 hr-1 aerobically. Nevertheless, there was negligible removal of phenol concentration observed in anaerobic condition. Similarly, the removal of anionic surfactants with rate of removal of 0.0454 hr-1 aerobically was observed, however there was insignificant removal in anaerobic condition. Sulphide concentration was observed to decrease in aerobic condition, while the concentration of sulphide was observed to increase significantly in anaerobic condition. The regressed sulphide prediction equation in anaerobic condition was proposed with coefficient a = 0.0265 and b= 0.5 and the forecasting model became ΔS = 0.0265 C 0.5 1.07(T-20) th (A/V). The model simulation study revealed that the Toxic Substance Model developed for river flow can be used with certain assumptions to predict the fate of anionic surfactants along the sewerage network very well. However, the model was found inadequate to predict the distribution of phenol along the sewer.

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