Performance of disc, conventional and automatic screen filters under rainbow trout fish farm effluent for drip irrigation system.

This study aims to investigate the performance of disc, conventional screen, and automatic screen filters when rainbow trout fish effluent is used for irrigation. The experiments were performed in a fish farm, located in the north-west of Iran. The disc and conventional screen filters were tested at pressures of 150 and 300 kPa, and the automatic screen filter at 200 and 300 kPa. The filtration experiments continued until the backwashing was reached. The results showed that (1) the initial head loss of disc and conventional screen filters was 40 kPa, while for the automatic screen filter was 5 kPa. (2) In the disc filter, with increasing working pressure, the filtered volume significantly (P < 0.05) increased from 9.7 to 14.5 m3 m-2 (10 kPa)-1, but for conventional and automatic screen filters, it was constant at 5.5 and 7.0 m3 m-2 (10 kPa)-1, respectively, and all of them had significant (P < 0.05) differences. (3) In the disc filter, with increasing the working pressure, the filtered volume to reach backwashing significantly (P < 0.01) increased from 80.9 to 104.4 m3 m-2, while in the conventional screen filter increased from 14.1 to 16.4 m3 m-2. This volume at two working pressures was 29.5 m3 m-2 for the automatic screen filter. These volumes were significantly different (P < 0.01) between filters. (4) The mass retention for the disc, conventional, and automatic screen filters were 28.88, 9.11, and 7.72 g min-1 m-2, respectively, and tended to increase at lower working pressures. Based on this index, the difference in the performance of the filters was significant (P < 0.01). (5) Overall, the best performance was for the disc filter, and after that was the automatic screen filters, but the period of time to operate for the filters until backwashing time was less than half an hour, which is not applicable under farm conditions.

Optimal virtual water flows for improved food security in water-scarce countries.

The worsening water scarcity has imposed a significant stress on food production in many parts of the world. This stress becomes more critical when countries seek self-sufficiency. A literature review shows that food self-sufficiency has not been assessed as the main factor in determining the optimal cultivation patterns. However, food self-sufficiency is one of the main policies of these countries and requires the most attention and concentration. Previous works have focused on the virtual water trade to meet regional food demand and to calculate trade flows. The potential of the trade network can be exploited to improve the cropping pattern to ensure food and water security. To this end, and based on the research gaps mentioned, this study develops a method to link intra-country trade networks, food security, and total water footprints (WFs) to improve food security. The method is applied in Iran, a water-scarce country. The study shows that 781 × 106 m3 of water could be saved by creating a trade network. Results of the balanced trade network are input to a multi-objective optimization model to improve cropping patterns based on the objectives of achieving food security and preventing water crises. The method provides 400 management scenarios to improve cropping patterns considering 51 main crops in Iran. Results show a range of improvements in food security (19-45%) and a decrease in WFs (2-3%). The selected scenario for Iran would reduce the blue water footprint by 1207 × 106 m3, and reduce the cropland area by 19 × 103 ha. This methodology allows decision makers to develop policies that achieve food security under limited water resources in arid and semi-arid regions.

Modeling groundwater quality by using hybrid intelligent and geostatistical methods.

Simulation of groundwater quality is important for managing water resources and mitigating water shortages, especially in arid and semiarid areas. Geostatistical models have been used for spatial prediction and interpolation of groundwater parameters. Recently, hybrid intelligent models have been employed for the simulation of dynamic systems. In this study, hybrid intelligent models, based on a neuro-fuzzy system integrated with fuzzy c-means data clustering (FCM) and grid partition (GP) models as well as artificial neural networks integrated with particle swarm optimization algorithm, were used to predict the spatial distribution of chlorine (Cl), electrical conductivity (EC), and sodium absorption ratio (SAR) parameters of groundwater. Results of the hybrid models were compared with geostatistical methods, including kriging, inverse distance weighting (IDW), and radial basis function (RBF). The latitude and longitude values of observation wells and qualitative parameters in three states of maximum, average, and minimum were introduced as input and output to the models, respectively. To evaluate the models, the root mean squared error (RMSE), the mean absolute error (MAE), and CC statistical criteria were used. Results showed that in the hybrid models, NF-GP with the lowest RMSE and MAE and highest CC was the most suitable model for the prediction of water quality parameters. The RMSE, MAE, and CC values were 107.175 (mg/L), 79.804 (mg/L), and 0.924 in the average state for Cl; were 518.544 (µmho/cm), 444.152 (µmho/cm), and 0.882 for electrical conductivity; and were 1.596, 1.350, and 0.582 for sodium absorption ratio, respectively. Among the geostatistical models, the kriging was found more accurate. Using the coordinates of wells will eventually allow the NF-GP to be used for more sampling and replace the visual techniques that require more time, cost, and facilities.