Ashraf Amin

Effective management of wastewater discharges requires understanding the spatial distribution of pollutants both within engineered infrastructure and in receiving environments. However, spatial data sparsity constrains comprehensive assessment. This critical review examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps for wastewater-derived pollutants. Moving beyond a simple compilation of methods, this paper provides a synthesizing framework that categorizes and evaluates interpolation techniques-from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models- based on their ability to address specific challenges in wastewater systems. A key contribution is a systematic review and meta-analysis following PRISMA guidelines, synthesizing evidence from 22 studies that directly compare interpolation methods for wastewater-relevant parameters (BOD₅, COD, nutrients, heavy metals) in both engineered systems and impacted water bodies. Results indicate that machine learning methods significantly outperform traditional approaches, with a pooled 21% reduction in RMSE compared to Ordinary Kriging (95% CI: 15–27%). However, subgroup analyses reveal context dependency: ML advantages are most pronounced for organic pollutants (29% reduction) and data-rich environments (27% reduction with n > 100), while geostatistical methods remain competitive for physical parameters (8% reduction, non-significant) and data-sparse scenarios (12% reduction with n < 50). Co-Kriging achieves 15% RMSE reduction over Ordinary Kriging when auxiliary variables are available. The review explores applications in pollutant tracking, infrastructure planning, and environmental impact assessment, highlighting how integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, a forward-looking research roadmap is presented, emphasizing hybrid modeling frameworks, digital twin integration, and improved uncertainty communication for decision support. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater-derived pollutants.

Effective management of discharged wastewater quality is crucial for maintaining public health, preserving aquatic ecosystems, and ensuring compliance with environmental regulations. However, spatial and temporal data sparsity remains a fundamental constraint. This review critically examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps to create continuous maps of wastewater quality parameters (e.g., BOD₅, COD, TSS, nutrients). Moving beyond a simple compilation of methods, this paper presents a comprehensive framework that categorizes and evaluates interpolation techniques, ranging from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models, based on their ability to address specific challenges in wastewater systems. A key contribution is a meta-analysis of 28 comparative studies, which quantitatively synthesizes evidence on the prediction accuracy (RMSE) of different methods. The results indicate that machine learning and hybrid models significantly outperform deterministic and basic geostatistical methods, with a pooled reduction in RMSE of 18.4% (95% CI: 12.1-24.3%) compared to Ordinary Kriging. We explore applications in pollutant tracking, impact assessment, and infrastructure planning, highlighting how the integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, we present a forward-looking roadmap for research, informed by our quantitative findings, emphasizing the need for hybrid modeling frameworks that leverage AI, the development of digital twins for wastewater networks, and the integration of uncertainty quantification into decision-support systems. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater quality.

Carbon nanotubes can be produced at high quality through hydrocarbon catalytic decomposition. In addition, hydrogen can be produced as a valuable byproduct at a competitive price. In this article, the loading of the active phase in the decomposition catalyst is optimized using natural gas as a widely available hydrocarbon. Natural gas decomposition was investigated using different nickel loadings. Natural gas decomposition, as a widely available hydrocarbon, is investigated by manipulating nickel loading to optimize the loading of the active phase in the decomposition catalyst.Optimizing the catalyst loading can achieve higher quality and yield of carbon nanotube. In addition, a higher carbon nanotube yield will maximize hydrogen production. Increasing the quality of produced carbon and the amount of hydrogen will improve the overall process economics. Nickel is a highly active catalyst for natural gas decomposition and has a higher carbon affinity compared to other metallic catalysts. Different nickel loadings were tested for natural gas decomposition. Optimization was used to calculate the optimum nickel loading based on the experimental results. The optimum nickel loading over alumina was 12.5%. The economic analysis of the process indicated that the optimum nickel loading is 30%.

The Volume 6 published for IJCI Journal.

Swelling is one of the distinct properties and a significant parameter in the characterization of the crosslinked hydrogels. Swelling investigation at different temperatures was conducted for the produced hydrogel of chitosan grafted acrylamide/acrylic acid. Peleg’s model is a two parameters model, which can be used to describe the absorption of water or swelling. The data show that the swelling degree increases with temperature until it reaches 60°C, after which it decreases as the temperature rises higher. At RT, 30, 40, 60, and 80 °C, the swelling degree values of 1.4, 1.5,1.4, 2.3 and 0.7 g water/g dry hydrogel were observed after 8 hours. The applied models provided a good agreement with the experimental data with high values of the coefficient of determination (𝑅2). The observed F values and the standard error indicate the validity of the proposed model to describe the swelling of hydrogel at different temperatures. The swelling rate and swelling degree can be expressed using a 4th degree polynomial. It was concluded that the exponential association equation model represents swelling characteristics better than the others.

Ni, Co, and Co–Ni bimetallic catalysts supported over SBA-15 and over SBA-15 doped with Zn or Ce oxides were prepared and tested in a methane dry reforming reaction. The loading of the metals in the catalyst was 5 wt % for either mono or bimetallic catalysts. The prepared catalysts were tested in a continuous-flow fixed-bed reactor at 800 °C under atmospheric pressure. XRD, TPR, TPD, and SEM characterization techniques were employed to investigate the catalytic properties of fresh catalysts. SEM and TGA were used to study the catalytic properties of spent catalysts. A remarkable effect on the reduction properties and catalytic performance of catalysts was observed after adding Zn and Ce. Over an 8 h test, Ni/SBA-15 showed the best activity and stability. The conversion was 90% for CH₄ and CO₂. Co–Ni/SBA-15 and Co–Ni/Ce–SBA-15 have shown a reasonable activity and stability. Selectivity of the Ni/SBA-15 catalyst was higher than all other catalysts as indicated by the H₂/CO ratio. Co/SBA-15 and Co–Ni/Zn–SBA-15 showed a low activity and selectivity. TPD–NH₃ profiles indicated that doping SBA-15 with Ce and/or Zn increased the catalyst acidic sites. Ni/SBA-15 is an excellent potential catalyst for commercial methane dry reforming processes.