Vol. 11 Issue 3 Jul.-Sep. 2020

Hoang Thi Trang, Kasemsan Manomaiphiboon*, Nkrintra Singhrattna and Nosha Assareh

Abstract: This study assessed the prediction performance of seven satellite precipitation products using the 3-hourly gauge data in 2014-2016 across Thailand, as the representative country in Upper Southeast Asia. They are Tropical Rainfall Measuring Mission near-real-time (TRMM_RT), gauge-adjusted TRMM (TRMM), Climate Prediction Center Morphing (CMORPH), Global Precipitation Measurement (GPM), Global Satellite Mapping of Precipitation - Standard (GSMaP_S), gauge-adjusted GSMaP (GSMaP_G), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN). The evaluation methods include bias, error, and correlation, event detection, phase of diurnality, variance partitioning, and wavelet coherence. No single product is universally superior over all aspects. Nevertheless, it is possible to suggest CMORPH as the best performing based on the majority of the methods. GPM and GSMaP_S also perform reasonably, except for incompatibility in the diurnality phase. These three products capture precipitation fluctuations to some extent, as robustly indicated by variance partitioning and wavelet coherence. GSMaP_G has the true strength in bias, error, and correlation but fails to capture the fluctuations. GSMaP_G and GPM detect the “light” class of precipitation event the best but only GPM continues to predict relatively well for the “moderate” and “heavy” classes. TRMM_RT appears to have relatively large bias and error and does not reasonably capture the fluctuations. PERSIANN has relatively large error and does not perform well for correlation and event detection.

Keywords: Rainfall, Harmonic analysis, Monsoon, Spectral decomposition, Autocorrelation.

Orhorhoro Ejiroghene Kelly*, Arhore Oghenegare A and Okuma Silas Oseme

Abstract: Biogas produced from municipal solid waste requires proper purification due to the presence of siloxanes and other impurities. Siloxanes are converted to silicon dioxide at the combustion temperature and this forms deposits on the combustion surfaces of the engine components. Thus, to prolong the equipment life, purification is requires. This study therefore focused on the determination and removal of siloxane in energy recovery of biogas from municipal solid waste. Air Toxics Method was adopted for sampling and the composition of siloxane and other impurities were determined using gas chromatograph. Two purification filters were developed using transparent plastic container, iron sponge, iron fillings, silica gel, activated carbon made from palm kernel shell and calcium oxide. The biogas produced was divided into sample A, sample B, sample C, and sample D and evaluated before and after purification for the presence of siloxanes and other possible impurities. It was observed that the percentage composition of produced methane increases with continuous anaerobic digestion of substrates while the percentage composition of siloxanes, and other impurities decrease with duration and extent of biomethanation. The results obtained also confirm the present of siloxanes in the four samples analyzed (0.628 mg/m3 for sample A, 0.638 mg/m3 for sample B, 0.613 mg/m3 for sample C, and 0.625 mg/m3 for sample D). However, after purification, the concentration of siloxane was reduced tremendously (0.628 mg/m3 for sample A, 0.638 mg/m3 for sample B, 0.613 mg/m3 for sample C, and 0.625 mg/m3 for sample D).


Keywords: Siloxane, Biogas, Purification, Municipal Solid Waste, Impurities.