Stormwater runoff is often contaminated by human activities. Stormwater discharge into water bodies significantly contributes to environmental pollution. The choice of suitable treatment technologies is dependent on the pollutant concentrations. Wastewater quality indicators such as biochemical oxygen demand (BOD 5 ), chemical oxygen demand (COD), total suspended solids (TSS), and total dissolved solids (TDS) give a measure of the main pollutants. The aim of this study is to provide an indirect methodology for the estimation of the main wastewater quality indicators, based on some characteristics of the drainage basin. The catchment is seen as a black box: the physical processes of accumulation, washing, and transport of pollutants are not mathematically described.Two models deriving from studies on artificial intelligence have been used in this research: Support Vector Regression (SVR) and Regression Trees (RT). Both the models showed robustness, reliability, and high generalization capability. However, with reference to coefficient of determination R 2 and root-mean square error, Support Vector Regression showed a better performance than Regression Tree in predicting TSS, TDS, and COD. As regards BOD 5 , the two models showed a comparable performance. Therefore, the considered machine learning algorithms may be useful for providing an estimation of the values to be considered for the sizing of the treatment units in absence of direct measures.

Hierarchical three-dimensional (3D) porous architecture Fe II Fe III layered double hydroxide (LDH) multiwall was grown on carbon-felt (CF) substrate via solvothermal process. The asdeposited Fe II Fe III LDH/CF cathode was composed of highly oriented and well crystallized interconnected nanowalls with high electrical conductivity and excellent catalytic activity over a wide pH range (pH 3 -9) for heterogeneous electro-Fenton (HEF) degradation of antibiotic sulfamethoxazole (SMT) in aqueous medium. Mineralization efficiencies (in terms of TOC removal) of ~97%, 93% and 90% was achieved at pH 3, 6 and 9 respectively for Fe II Fe III cathode during HEF treatment of 0.2 mM SMT solution at applied current density of 7.5 mA cm -2 using Ti4O7 anode. Comparative electro-Fenton (EF-Fe 2+ ) with 0.2 mM Fe 2+ or electrooxidation with H2O2 production (EO-H2O2)studies using raw CF cathode at similar experimental conditions showed relatively lower mineralization with highest TOC removal efficiency of 77% and 64% obtained at pH 3 for EF-Fe 2+ and EO-H2O2 respectively. Oxidative degradation of SMT in HEF system was by (i) Ti4O7( • OH) generated at anode surface at all pH studied, (ii) surface catalyzed process and (iii) contribution from homogeneous catalyzed process at pH 3 due to leached iron ions. The prepared Fe II Fe III LDH/CF exhibited excellent catalytic stability with good reusability up to 10 cycles of 4 h treatment at pH 6. Initial SMT solution showed relatively high toxicity but total detoxification of the solution was attained after 8 h of treatment by HEF with Fe II Fe III LDH/CF cathode. HEF with Fe II Fe III LDH/CF cathode is an exciting technique for remediation of organic contaminated wastewater.

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This work investigated the effect of three different chemical pretreatment methods on the biogas production\ud from the anaerobic digestion of wheat straw. The lignocellulosic material was separately pretreated\ud using i) the organic solvent N-methylmorpholine N-oxide (NMMO) at 120°C for 3 h, ii) the\ud organosolv method, employing ethanol as the organic solvent at 180°C for 1 h and iii) using an alkaline\ud pretreatment with NaOH at 30°C for 24 h. All the pretreatments were effective in increasing the biomethane\ud production yield of wheat straw. In particular, the cumulative biomethane production yield of\ud 274mL CH4/g VS obtained with the untreated feedstock was enhanced by 11% by the NMMO pretreatment\ud and by 15% by both the organosolv and alkaline pretreatment. The three pretreatment methods had\ud a different impact on the chemical composition of the straw. NMMO hardly changed the amount of\ud carbohydrates and lignin present in the original feedstock. Organosolv had a major impact on dissolving\ud the hemicellulose component, whereas the alkaline pretreatment was the most effective in removing the\ud lignin fraction. In addition to the increased biogas yields, the applied pretreatments enhanced the kinetics\ud of biomethane production

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