Biological treatment systems face many challenges in winter to reduce the level of nitrogen due to low temperatures. The present work aimed to study an electrochemical treatment to investigate the effect of applying an electric voltage to wastewater to reduce the ammonium nitrogen and COD (chemical oxygen demand) in domestic wastewater. This was done by using an electrochemical process in which a platinum-coated titanium material was used as an anode and stainless steel was used as a cathode (25 cm² electrode area/500 mL). Our results indicated that the removal of ammonium nitrogen (NH₄⁺–N) and the lowering of COD was directly proportional to the amount of electric voltage applied between the electrodes. Our seven hour experiment showed that 97.6% of NH₄⁺–N was removed at an electric voltage of 5 V, whereas only 68% was removed with 3 V, 20% with 1.2 V, and 10% with 0.6 V. Similarly, at 5 V, the removal of COD was around 97.5%. Over the seven hours of the experiment, the pH of wastewater increased from pH 7.12 to pH 8.15 when 5 V was applied to the wastewater. Therefore, electric voltage is effective in the oxidation of ammonium nitrogen and the reduction in COD in wastewater.

This study aims to understand how different reference periods (i.e., calibration periods) of climate data used to estimate drought indices influence regional drought assessments. Specifically, we investigate the influences of different reference periods on historical drought characteristics, such as the trend, frequency, intensity and spatial extent, using the standardized precipitation evapotranspiration index (SPEI) with a 12-month lag (SPEI-12), which was estimated from the datasets of the Climate Research Unit (CRU) and the University of Delaware (UDEL). For the 1901–1957 (P1) and 1958–2014 (P2) estimation periods, three different types of reference periods are used to compute the SPEI: P1 and P2 together, P1 and P2 separately and P1 only. Focusing on East Asia, Europe, the United States and West Africa, we find that the influence of the reference period is significant in East Asia and West Africa, with dominant drying trends from P1 to P2. The reference period influenced the assessment of drought characteristics, particularly the severity and spatial extent, whereas the influence on the frequency was relatively small. Finally, self-calibration, which is the most common practice for indices such as the SPEI, tends to underestimate the drought severity and spatial extent relative to the other approaches used in this study. Although the conclusions drawn in this study are limited by the use of two global datasets, they highlight the need for clarification of the reference period in drought assessments to better understand regional drought characteristics and the associated temporal changes, particularly under climate change scenarios.

The present work aimed to examine the feasibility of using artificial neural network (ANN) based models to obtain accurate estimates of nitrate loads in river basins, which is an important parameter for water quality management. Both Single ANN (SANN) and Ensemble ANN (EANN) models were used to obtain the load estimations for five river basins in the Midwest United States. These basins included the Cuyahoga, Raisin, Sandusky, Muskingum, and Vermilion basins in Michigan and Ohio. Further, canonical correlation analysis (CCA) was applied to the ANN models to improve the performance. The k-fold cross-validation method was then utilized to evaluate the proposed models based on two statistical indices, namely, the rRMSE and rBAIS, and the estimates were compared for four different k values (k = 3, 5, 7, and 10). According to the results, the EANN model seemed to produce better load estimations than the SANN model, and the CCA based EANN model tended to produce the best estimates among all of the proposed models in this study. The box plot data for the rRMSE index were also investigated, and the plot results indicated that increasing values of k tended to generate better estimates. Thus, the use of k = 10 is recommended for load estimations since this value was associated with better performances and less biased estimates.