Qasem Abdelal

Wastewater treatment by electrocoagulation (EC) using waste‐derived electrodes is a promising solution, but its viability in irrigation reuse remains unclear. This work presents a quantitative framework integrating agrometeorological analysis (FAO‐56 method) and waste information into energy and electrochemical models (Faraday's laws of dissolution) for viability assessment. The circular reuse framework was evaluated at a study site comprising a large institution surrounded by irrigated trees. Onsite experiments showed that electrochemical dosages as low as 20 g/m ³ of aluminum (Al) scrap, or 60 g/m ³ for iron (Fe) scrap, achieved significant removals (75%–94%) of COD, BOD, TSS, and turbidity. Onsite metal waste generation (593 kg/month) could accommodate ~98,000 m ² of surrounding irrigated lands. Crop production capacities of scrap metals (food per scrap) were estimated to reach 23.3 kg/kg using Al scrap. Based on crop water dynamics, theoretical fixed energy demand for renewable energy integration was calculated to be 0.58 kWh/m ³ . Validation of model components revealed that modeling scrap electrode consumption comprised minimal uncertainty compared with other components like crop water demand. The reuse framework shows promising potential as a WEFE nexus application but raised some socioeconomic concerns such as impacts on scrap scavengers' livelihoods. Prior to wider adoption, qualitative assessments are necessary to address heavy metals removal, soil contamination, nutrient content, and microbial indicators. Pilot‐scale experiments are also encouraged to gain improved insights.

Accurate monitoring of chlorophyll-a (Chl-a) is critical for assessing eutrophication and water quality in inland aquatic ecosystems. While integrating Sentinel-2 MultiSpectral Instrument (MSI) imagery with machine learning (ML) is widely used for water quality assessment, challenges remain in optimizing Chl-a retrieval in optically complex small-scale reservoirs, particularly in semi-arid environments where traditional models often fail. This study introduces a machine learning (ML)-based approach to enhance Sentinel-2 MSI Chl-a retrieval, systematically evaluating six ML models: Artificial Neural Networks (ANN), CatBoost (CB), random forest (RF), ridge regression (RR), support vector regression (SVR), and extreme gradient boosting (XGB). By integrating Sentinel-2 MSI reflectance data with in-situ measurements from reservoirs exhibiting diverse trophic conditions, this study not only compares model performance but also applies feature importance analysis to refine spectral band selection for improved Chl-a retrieval. Among the models, RF demonstrated the highest predictive accuracy (R² = 0.93 calibration, R² = 0.91 validation), outperforming CB and XGB, whereas linear models such as RR and SVR were inadequate for capturing the nonlinear spectral relationships of Chl-a. The results highlight the advantages of ensemble-based ML models in handling complex water quality datasets, particularly in semi-arid and data-scarce regions. The proposed framework provides a robust, data-driven approach for integrating Sentinel-2 MSI imagery with AI techniques, supporting real-time water quality assessment and environmental management in diverse aquatic ecosystems. This research advances the application of ML for remote sensing-based water quality monitoring by systematically assessing model reliability, feature selection, and efficient application in semi-arid small-scale reservoirs. The proposed approach enhances the scalability of ML-driven remote sensing, offering a transferable framework for Chl-a estimation in data-scarce and environmentally vulnerable regions.

In water-scarce regions such as Jordan, accurate tracking of water flows is critical for informed water management. This study applied the Water Accounting Plus (WA+) framework using open-source remote sensing data from the FAO WaPOR portal to develop agricultural water accounting (AWA) for the Amman–Zarqa Basin (AZB) during 2014–2022. Inflows, outflows, and water consumption were quantified using WaPOR and other open datasets. The results showed a strong correlation between WaPOR precipitation (P) and rainfall station data, while comparisons with other remote sensing sources were weaker. WaPOR evapotranspiration (ET) values were generally lower than those from alternative datasets. To improve classification accuracy, a correction of the WaPOR-derived land cover map was performed. The revised map achieved a producer’s accuracy of 15.9% and a user’s accuracy of 86.6% for irrigated areas. Additionally, ET values over irrigated zones were adjusted, resulting in a fivefold improvement in estimates. These corrections significantly enhanced the reliability of key AWA indicators such as basin closure, ET fraction, and managed fraction. The findings demonstrate that the accuracy of P and ET data strongly affects AWA outputs, particularly the estimation of percolation and beneficial water use. Therefore, calibrating remote sensing data is essential to ensure reliable water accounting, especially in agricultural settings where data uncertainty can lead to misleading conclusions. This study recommends the use of open-source datasets such as WaPOR—combined with field validation and calibration—to improve agricultural water resource assessments and support decision making at basin and national levels.

Wastewater treatment by electrocoagulation (EC) using waste-derived electrodes is an emerging and promising solution for closing resource loops at urban-irrigated intersections. However, there is a knowledge gap in integrating crop water dynamics into this application. This article presents a framework to integrate agrometeorological analysis (FAO56 model) and waste information into energy and electrochemical models (Faraday’s laws of dissolution). The model was parameterized for a study site comprising a large institution surrounded by irrigated trees. On-site experiments showed that electrochemical dosages of aluminum (Al) and iron (Fe) scrap, as low as 0.2–0.6 g/m³, achieved significant removals (75–94%) of COD, BOD, TSS, and turbidity, and met irrigation standards. Onsite metal waste generation (593 kg/month) could accommodate ~ 98,000 m² of surrounding irrigated lands. Crop production capacities of scrap metals (food per scrap) were estimated to reach 23.3 kg/kg, specifically for Al scrap. Energy demand could be maintained steady at 0.58 kWh/m³ by integrating crop water dynamics, allowing flexible renewable energy integration. Validation of model components revealed that estimating crop water demand comprised a larger uncertainty than modeling scrap electrode consumption. The reuse framework shows promising potential as a WEFE nexus application but raises some socioeconomic concerns such as impacts on scrap scavengers’ livelihoods. Pilot-scale experiments and wider assessments are strongly encouraged to gain improved insights.

Improved understanding of the interactions of soil properties can reduce calibration and technical complexities related to soil moisture measurement techniques. This can improve technology adoption beyond researchers. This paper investigates a previously unexplored factor: the isolated impact of soil particle size on different moisture measurement techniques. Soil from a semiarid region was sieved to produce 2 sets of samples; a set with a larger fraction of course particles (sand) and a set of samples with a larger fraction of finer particles, both being of similar properties and having controlled moisture levels. Resulting variations in Time Domain Transmission (TDT), capacitance, and resistive measurements were analyzed. Analysis revealed that particle size, even from the same soil, had a significant effect (p < 0.05) on moisture measurements through TDT and resistivity. The interaction between particle size and moisture level significantly affected all measurement types (p < 0.05) but did not affect the repeatability of sensor measurements (p > 0.1). Dielectric measurements (TDT/capacitance) produced stronger correlations with soil moisture (R: 0.85–0.99). TDT produced slightly weaker correlations in course samples but showed higher accuracy (error: 1–10%) than capacitance. Unlike dielectric measurements, resistivity responded mainly to soil water on a mass basis, not volumetric soil moisture. The results add further accumulating evidence that multiple pre-calibration settings for courser and finer soils are needed. Furthermore, accuracy metrics for lower and higher moisture ranges should be reported by manufacturers for improved technology selection by soil scientists and practitioners. This can support monitoring indicators of sustainable development goals relevant to land and water degradation.

The cheap conductivity‐based sensors favoured by farmers for soil moisture monitoring remain largely ignored by researchers because of their low accuracy, which results in high uncertainty regarding their utility for irrigators. In this work, conductivity measurements were compared with dielectric permittivity measurements in moisture ranges relevant to different applications. The results showed that the permittivity measurements captured moisture variability well ( R ² > 0.8) throughout the full range tested (0%–35%), which is consistent with the literature. Conductivity measurements consistently distinguished dry from wet conditions ( p < 0.0001) and reflected moisture variability in lower ranges ( R ² > 0.5) but not in higher ranges (>20%). This is problematic because important monitoring thresholds such as field capacity and saturation are in the upper moisture ranges. Conductivity measurements were found to lack any meaningful utility in most applications except those relevant to distinguishing dry from wet conditions and indicating lower‐range moisture patterns, such as monitoring in arid environments. This gives some merit to conductivity sensors considering their very low cost if corrosion is minimised. The described evaluation approach is suggested as an example for developers, labs and extension services to better communicate potential sensor utilities and restrictions to practitioners to improve their accessibility to decision support technologies.