Search

The uses of clay pipes in irrigation water management are becoming popular, especially in arid and semi-arid soils. The study examined clay and zeolite materials for irrigation pipes, and this paper reported characteristic properties of these materials. Hydrometer, pychnometer and core sampler methods were for soil physical properties determination. Consistency tests using (Atterberg method) and analytical techniques (Scanning Electron Microscopy, SEM and Energy Dispersive Spectroscopy, EDS) for samples analyses. The results indicate the soil to contain 11 % sand, 34 % silt and 55 % clay fractions and texturally classified as clay with a particle density of 1.58 g/cm3 and bulk density 2.43 g/cm3 respectively. Addition of zeolite at 3:1 clay/zeolite mix ratio shows liquid limit (LL) and plastic (PL) values to decreases from 50.7% to 43.7% and 27.6% to 27.3% while plasticity index, (PI) change from 23.2 to 16.7 respectively. The shrinkage rate decreases from 11.67% for raw clay to 8.92 % for the treated sample. The EDS analysis shows both clay and zeolite samples to contain carbon, silica (SiO2) and alumina (Al2O3) as the major constituents with ferric oxide (Fe2O3), potassium oxide (K2O) and cobalt (Co) as the minor constituents. The major constituents contribute 89.26 and 94.4% while minor contribute 10.74 and 5.59 % in clay and zeolite samples. Modifying clay improved its workability, reduces cracking potential and absorption capacity and performance of porous clay pipes.

Advanced human activities, including modern agricultural practices, are responsible for alteration of natural concentration of nitrogen compounds in rivers. Future prediction of nitrogen compound concentrations (especially nitrate-nitrogen and ammonia-nitrogen) are important for countries where household water is obtained from rivers after treatment. Increased concentrations of nitrogen compounds result in the suspension of household water supplies. Artificial Neural Networks (ANNs) have already been deployed for the prediction of nitrogen compounds in various countries. But standalone ANN have several limitations. However, the limitations of ANNs can be resolved using hybrid models. This study proposes a new ACO-ENN hybrid model developed by integrating Ant Colony Optimization (ACO) with an Elman Neural Network (ENN). The developed ACO-ENN hybrid model was used to improve the prediction results of nitrate-nitrogen and ammonia-nitrogen prediction models. The results of new hybrid models were compared with multilayer ANN models and standalone ENN models. There was a significant improvement in the mean square errors (MSE) (0.196→0.049→0.012, i.e. ANN→ENN→Hybrid), mean absolute errors (MAE) (0.271→0.094→0.069) and Nash–Sutcliffe efficiencies (NSE) (0.7255→0.9321→0.984). The hybrid model had outstanding performance compared with the ANN and ENN models. Hence, the prediction accuracy of nitrate-nitrogen and ammonia-nitrogen has been improved using new ACO-ENN hybrid model.

Water productivity (WP) is a key indicator of agricultural water management, since it affects the quantity of water used for crop yield in various management scenarios. This study evaluated the WP of irrigated rice due to a changing climate in the Northwest Selangor Rice Irrigation Scheme (NSRIS) by using field experimental data and the FAO-AquaCrop Model. Pertinent soil, water, climate, and crop data were acquired by executing a field investigation during the off-season (dry season, January–April) and main season (wet season, July–October) in 2017. The AquaCrop 6.0 model was calibrated and validated using the measured data. A Climate-smart Decision Support System (CSDSS) with an ensemble of 10 Global Climate Models (GCMs) was used to downscale climate variables under RCP4.5, RCP6.0, and RCP8.5 emission scenarios during baseline (1976 to 2005) and future (2020 to 2099) periods. The AquaCrop model fairly predicted rice yields under field conditions with root-mean-square error (RMSE), mean absolute error (MAE), prediction error (PE) and index of agreement (d) between the observed and estimated yields of 0.173, 0.157, −0.31 to 5.4 and 0.78, respectively for the off-season; and 0.167, 0.127, −5.6 to 2.3 and 0.73, respectively for the main season. It predicted a 10% decrease in actual crop evapotranspiration (ETc) in both crop seasons in the future. The WP of rice based on total water input (WPIrr+RF), applied irrigation (WPIrr), and actual crop evapotranspiration (WPETc) will likely increase by 14–24%, 14–19%, and 17–29%, respectively under the three RCP emission scenarios in the off-season. The likely increase in WP for the corresponding base is 13–22%, 15–24%, and 14–25% in the main season. Various agronomic management options linked to WP will most likely become important in making crucial decisions to cope with the risk of impacts on climate change.

Nitrogen loss from agricultural fields results in contamination of ground and surface water resources due to leaching and runoff, respectively. Nitrogen transport dynamics vary significantly among agricultural fields of different climates, especially in the tropical climate. This study intended to evaluate the rainfall impact on nitrogen distribution and losses under tropical rain-fed conditions. The study was carried out in a sweet corn field for two growing seasons at the Malaysian Agricultural Research and Development Institute (MARDI) research field. The HYDRUS-1D numerical model was used to simulate nitrogen transport dynamics in this study. The observed nitrogen concentrations were used for calibration and validation of the model. Total nitrogen input to sweet corn was 120 kg/ha for both seasons. Nitrogen losses through surface runoff and leaching were dominating pathways. Surface runoff accounted for 35.3% and 22.2% of total nitrogen input during the first and second seasons, respectively. The leaching loss at 60 cm depth accounted for 4.0% (first season) and 18.5% (second season). The crop N uptake was 37.5% and 24.9% during the first and second seasons, respectively. Nitrate was the dominant form of N uptake by the crop that accounted for 83.6% (first season) and 78.5% (second season). The HYDRUS-1D simulation results of nitrogen concentrations and fluxes were found in good agreement with observed data. The overall results of simulation justified the HYDRUS-1D for improved fertilizer use in the tropical climate.

The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water treatment plants depend on the concentration of nitrogen in streams. Considering the stochastic nature and the various hydrological variables upon which nitrogen concentration depends, a predictive model should be efficient enough to account for all the complexities of nature in the prediction of nitrogen concentration. For two decades, artificial neural networks (ANNs) and other models (such as autoregressive integrated moving average (ARIMA) model, hybrid model, etc.), used for predicting different complex hydrological parameters, have proved efficient and accurate up to a certain extent. In this review paper, such prediction models, created for predicting nitrogen concentration, are critically analyzed, comparing their accuracy and input variables. Moreover, future research works aiming to predict nitrogen using advanced techniques and more reliable and appropriate input variables are also discussed.

AMD stands for Acid Mine Drainage. AMD results from the mining activities involved during mineral exploration. When minerals interact with each other in the presence of atmospheric oxygen and water AMD is the end product. AMD also accumulates from the weathering of sulphide minerals through the process of oxidation and hydrolysis. This case study was being carried out for the investigation of the occurrence of Acid Mine Drainage (AMD) and contamination of the heavy metals in the active and abandoned mining sites in Bentong, Pahang. This case study deals with the traces of heavy metal toxins found in the water bodies causing the discoloration of the water catchment, downstream of the Old Repas dam. This discoloration of water proved the occurrence of AMD which is a very bad pollutant. To get a better understanding of the situation, site reconnaissance and survey were carried out to examine those area closely that have been affected by AMD. Two different sampling points were recognised which had traces of AMD contamination. Water testing and sampling were to be carried on these particular sampling points. Four in-situ parameters and five heavy metals were selected for sampling. These parameters included PH of water, dissolved oxygen content. Total dissolved solids and Ammoniacal Nitrogen while the heavy metals included Zinc, Lead, Tin, Mercury and Arsenic. A total of four samples were collected on two different sampling points and on two different sampling days. Sampling was carried out, these parameters were measured and surface water with sediments were also collected and analysed for heavy metals. Based on the investigations done during this project, these sampling points had acidic PH values which indicate the occurrence of the Acid Mine Drainage, which had been a major find in this case study. During the Heavy Metal analysis concentration of Pb, Sn, Hg, As & Zn was observed to exceed the allowable standard limits for treated water by the Malaysian Ministry of Health. I concluded this with the believe that there was an urgent need for appropriate treatment of this site to prevent further environmental deterioration.

Bangladesh is a nation of over 125 million population with a geographical area of 1,48,393 km2 and located on the Ganges, Brahmaputra, and Meghna Delta. The major part of the country is now severely affected by mass poisoning from arsenic in groundwater. The latest statistics on the arsenic contamination in groundwater indicates that 59 out of 64 districts, about 80% of the total area of Bangladesh and about 40 million people are at risk. Presently about 116 million people depend on water from tube-wells for drinking, cooking, washing and bathing and about 60 million people are drinking and using arsenic contaminated groundwater. Arsenic level now ranges from 0 ppm to 0.98 ppm and the maximum permissible level of arsenic in Bangladesh drinking water is 0.05 ppm. Average values of different tubewells data on thana census blocks have been used in this study. A spatial and temporal distribution of Groundwater Arsenic Contamination Intensity (GACI) has been developed using Geographical Information System (GIS) in order to assist decision making process in some critical areas, particularly to protect human health. The digitized thana boundary maps with longitude-latitude coordinates storing information in the form of metadata has also been developed to visualize GACI information in thana census blocks of Bangladesh. The MapBasic Professional 4.5 has used for developing user-interface design and MapInfo Professional 4.5 for the visualization of spatial information. The user-interface GIS approach can help policy makers deciding proper groundwater utilization and taking necessary steps to supplying safe drinking water to the domestic and industrial areas.