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Accurate prediction of irrigation requirements ensures that water is applied only when necessary, reducing wastage and conserving this precious resource. This study provides a probabilistic framework for determining the irrigation requirements of crops, referred to as the Irrigation Factor (IF). IF was calculated based on three indicators - soil moisture (SM), leaf area index (LAI), and evapotranspiration (ET). Irrigation requirement is determined based on a three-step methodology. First, relevant variables for each indicator are identified using a Random Forest regressor, followed by the development of a Long Short-Term Memory (LSTM) model to predict the three indicators. Second, errors in the simulation are calculated for each indicator by comparing the predicted and actual values in the historical time period, which are then used to calculate the error weights (normalized) of the three indicators for each month to also capture the seasonal variations. Third, we calculate the lower and upper limits by adding the error values (5th and 95th percentiles) to a simulated value. Using these values, we determine the mean, minimum, and maximum levels of irrigation requirement based on the levels' threshold values. To determine the final levels of irrigation requirement at a daily time scale, we multiply the calculated levels (mean, minimum, and maximum) for each indicator by their respective weights. The outcome derived from the test case indicated that while certain variables exhibited no demand for water, there was a necessity for irrigation in other cases, and vice versa. This holistic approach to irrigation scheduling helps to ensure that plants receive adequate water while minimizing water wastage and promoting sustainability. It is especially valuable for agricultural operations, where optimizing water usage is essential economically and environmentally. Highlights: Irrigation Factor (IF) was developed – a probabilistic framework to determine the irrigation requirements of crops. IF was computed by combining three key indicators covering the soil moisture deficit, plant water stress, and atmospheric demand. Combining multiple indicator variables arguably enhanced the robustness of the framework by overcoming the shortcomings within individual variables. The probabilistic nature of the IF framework additionally provided crucial information about the mean, minimum, and maximum irrigation requirements, enabling more informed decision-making, particularly in uncertain scenarios. The easy-to-use IF framework also captures seasonal variations in irrigation requirements, aiding more realistic decision-making. [ABSTRACT FROM AUTHOR]

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO2). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM’s predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil–plant–atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO2 m−2 day−1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m−2 day−1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development. [ABSTRACT FROM AUTHOR]

This study was conducted to investigate the impact of intermittent feeding on performance, clogging, and gaseous emission on macrophyte assisted vermifiltration (MAVF) based treatment system. Synthetic slaughterhouse wastewater was applied to two different integrated vertical flow based MAVFs. Triplicates were used throughout the study. Eisenia fetida earthworms were added to MAVFs, and Carex muskingmenis plants were planted. Wastewater was applied to the reactors on 1) intermittent (8 h/day) (IMAVF) and 2) continuous (24 h/day) (CMAVF) basis. The average chemical oxygen demand, total nitrogen, and total phosphorous removals achieved by the IMAVF were 80.2 ± 1.6%, 53.9 ± 1.3% and 66.5 ± 1% respectively, and 68.3 ± 1.3%, 61.2 ± 1.4%, and 60.5 ± 1.4% by the CMAVF, respectively. The diffusion of air to the bedding of IMAVFs during no-flow conditions facilitated higher organics oxidation, adsorption of phosphorous, nitrification, and ammonification. At the end of the study, hydraulic conductivity of IMAVF and CMAVF were found to be 0.036 cm/s and 0.037 cm/s, respectively. CO2, CH4 and N2O emissions from IMAVF were 245.5 ± 38.0 mg C/m2, 5.0 ± 4.6 mg C/m2 and 2513.5 ± 2629.9 μg N/m2 respectively, while CO2, CH4 and N2O emissions from CMAVF were 123.3 ± 14.5 mg C/m2, 74.8±45.2 mg C/m2 and 328.4 ± 93.4 μg N/m2, respectively. Intermittent application of influent could be considered for improving the performance and lifespan of MAVFs, causing lower environmental footprints. [ABSTRACT FROM AUTHOR]

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration ranges of nitrate and pesticide. This study evaluates the classification performance of the models for two, three, and four class scenarios and compares them with the corresponding regression models. The study also examines the issue of class imbalance and tests the efficacy of three class imbalance mitigation techniques: oversampling, weighting, and oversampling and weighting, for all the scenarios. The models' performance is reported using multiple metrics, both insensitive to class imbalance (accuracy) and sensitive to class imbalance (F1 score and MCC). Finally, the study assesses the importance of features using game-theoretic Shapley values to rank features consistently and offer model interpretability. [ABSTRACT FROM AUTHOR]

Agricultural crops have a long history of being irrigated with recycled wastewater (RW). However, its use on vegetable crops has been of concern due to the potential prevalence of microcontaminants, such as pharmaceuticals and personal care products (PPCPs) in the latter, which represents a possible health hazard to consumers. We investigated the uptake of three PPCPs (atenolol, diclofenac, and ofloxacin), at three different concentrations in irrigation water (0.5, 5, and 25 μg L) in relation to three varying volumetric soil moisture depletion levels of 14 % (−4.26 kPa), 10 % (−8.66 kPa), and 7 % (−18.37 kPa) by various vegetable crop species. Experiments were conducted in a split-split block completely randomized design. PPCPs were extracted using a developed method of accelerated solvent extraction and solid phase extraction and analyzed via liquid chromatography mass spectrometry (LCMS). Results indicate that all treated crops were capable of PPCP uptake at nanogram per gram concentrations independent of the applied soil moisture depletion levels and PPCP concentrations. Ofloxacin was the chemical with the highest uptake amounts, followed by atenolol and then diclofenac. Although the results were not statistically significant, higher concentrations of PPCPs were detected in plants maintained under higher soil moisture levels of 14 % (−4.26 kPa). [ABSTRACT FROM AUTHOR]

Providing potable water in developing countries and following a natural disaster is a challenge. Among the different low-cost water treatments, slow sand filtration (SSF) has been widely used; however, it is not clear whether SSF can also be used with an influent water having high bacterial loads (>10 MPN/100 mL). These high levels of bacteria in the feed water can be naturally present in developing countries or can occur as a result of sewage spills or after a natural disaster. Two SSF units and a point of use device, an in-line UV unit, were tested using feed water with high bacterial loads. The study confirmed that SSF can be effectively used to treat feed water with high bacterial loads. Approximately 60 % of the bacterial removal occurred in the top 5 cm of the SSF unit where a biolayer is found. Bacterial removal efficiency was related not only to the quality of the biolayer (i.e., whether it was completely or partially developed) but also to the SSF unit itself (i.e., age of unit). The ability of the biolayer to remove bacteria appears to be marginally impacted by the water used to develop the biolayer. During the restoration period that followed the addition of 20 % primary effluent to the feed water, higher bacterial removal occurred in the biolayer developed with stream water and 1 % primary effluent than in the biolayer developed with stream water alone. An in-line UV unit consistently removed total coliforms and Escherichia coli up to 10 MPN/100 mL from the effluent of a SSF unit. [ABSTRACT FROM AUTHOR]

Following a natural disaster, access to safe drinking water by the affected population is a high priority. Low cost water purification systems, which can be used for both short-term (immediate) and long-term (sustainable) response to serve the needs of the affected communities, are ideal for these scenarios. The University of Hawaii has developed three low cost water purification technologies for use during humanitarian assistance and disaster relief (HA/DR) missions. A UH team participated in joint USA and partner nation training exercises, such as Crimson Viper 2010 and 2011, organized by the Marine Corps Forces Pacific Experimentation Center (MEC) in Sattahip, Thailand, to demonstrate the effectiveness of these technologies to purify water from local sources. Three technologies were selected for Crimson Viper 2010: (1) a backpack filter unit, (2) a bicycle pump powered reverse osmosis (RO) unit, and (3) a model slow sand filtration unit. For Crimson Viper 2011, improved versions of the backpack and RO units were deployed. This article discusses and evaluates the results obtained during the demonstration of the three technologies at these exercises. [ABSTRACT FROM AUTHOR]

Subsurface coherent structures and surface temperatures are investigated using simultaneous measurements of particle image velocimetry (PIV) and infrared (IR) thermography. Results for coherent structures from acoustic streaming and associated heating transfer in a rectangular tank with an acoustic horn mounted horizontally at the sidewall are presented. An observed vortex pair develops and propagates in the direction along the centerline of the horn. From the PIV velocity field data, distinct kinematic regions are found with the Lagrangian coherent structure (LCS) method. The implications of this analysis with respect to heat transfer and related sonochemical applications are discussed. [ABSTRACT FROM AUTHOR]

Riverbank filtration (RBF) has been used for many decades in Europe and the United States to provide drinking water to communities located on riverbanks. In India, the development of RBF has the potential to provide drinking water to many cities located on the Ganga Plains currently using surface water as a source for their public water supply. Water diversion for irrigation and discharge of wastewater to rivers with extremely low flows has aggravated the water supply situation for many Indian cities using surface water. A number of Indian cities, with source waters of significantly varying quality, are already using RBF. In most of these cities no significant additional treatment is provided to the filtrate for their water supply. The objective of this article is to examine selected operating bank filtration sites in India (that have been investigated since 2005) and to elucidate additional potential RBF sites based on water problems and hydrogeologic suitability. A summary of selected operational RBF systems in Ahmedabad, Delhi, Haridwar, Mathura, Medinipur and Kharagpur, Nainital, Patna and Srinagar and their ability to produce potable water is provided. Analysis of the suitability of RBF for Allahabad, Bhubaneswar, Guwahati, and Vijayawada, based on hydrogeology and land use, is also provided. [ABSTRACT FROM AUTHOR]

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This glasshouse study is aimed at evaluating tropical plants for phytoremediation of petroleum hydrocarbon-contaminated saline sandy subsurface soils. Tropical plants were selected for their ability to tolerate high salinity and remove No. 2 diesel fuel in coastal topsoil prior to further investigation of the phytoremediation feasibility in deep contaminated soils. The residual petroleum-hydrocarbon contaminant at the John Rogers Tank Farm site, a former petroleum storage facility, at Hickam Air Force Base, Honolulu, Hawaii, is located in a coastal area. It lies below a layer of silt in the subsurface, in loamy sand characterized by moderate salinity and high pH. Little is known regarding the ability of tropical plants to remediate petroleum hydrocarbon-contaminated subsurface soil in Hawaiian and other Pacific Island ecosystems although suitable plants have been identified and utilized for bioremediation in surface soil or marine sediments. Methods. The experiments were conducted in long narrow pots under glasshouse conditions in two phases. A preliminary experiment was done with nine tropical plants: kiawe (Prosopis pallida), milo (Thespesia populnea), common ironwood (Casuarina equisetifolia), kou (Cordia subcordata), tropical coral tree (Erythrina variegata), false sandalwood (Myoporum sandwicense), beach naupaka (Scaevola sericea), oleander (Nerium oleander), and buffelgrass (Cenchrus ciliaris). These plants were screened for resistance to high salinity treatment (2% NaCl) and two diesel fuel levels (5 and 10 g No. 2 diesel fuel/kg soil) in separate treatments. Plants that showed good tolerance of both factors were further evaluated in a second phase for their efficacy in the phytoremediation of diesel-fuel petroleum hydrocarbons under moderate salinity treatment (1% NaCl). Results. Tropical coral tree and buffelgrass were susceptible to either 2% NaCl or diesel fuel at 10 g/kg soil, but tolerant of diesel fuel at 5 g/kg soil. Kiawe, milo, kou, common ironwood, N. oleander, beach naupaka and false sandalwood were tolerant of high salinity (2% NaCl) or high diesel fuel level (10 g/kg soil). These seven plants were also tolerant of the combined adverse effects of a moderate salinity (1% NaCl) and 10 g diesel fuel/kg soil. Three trees, kiawe, milo and kou significantly accelerated the degradation of petroleum hydrocarbons in the soil spiked with 10 g diesel fuel/kg soil under a moderate salinity treatment (1% NaCl). Thus the tropical woody plants, kiawe, milo and kou showed potential for use in phytoremediation of petroleum hydrocarbons in coastal tropical soils. Two fast growing trees, milo and kou, appeared promising for further phytoremediation evaluation in experiments that simulate the soil profile at the field site. [ABSTRACT FROM AUTHOR]

The author reflects on the global potential of riverbank filtration (RBF) in relation to the demand for piped drinking water. RBF is said to be an excellent treatment technology for drinking water production. It has been used in Europe, the U.S., Germany and other countries. The author argues that RBF has global significance because it can help water utilities to meet water demands. However, he claims that hydrogeologic data and economic and engineering analysis are needed to implement RBF systems.