In this study we have evaluated three advanced water treatment technologies in laboratory conditions, electrochemical (EC), fluidized bed (FB) and nanocomposite-based systems. The performance of the three technologies were evaluated based on several characteristics, such as pollutant removal efficiency, operating cost (USD/m$^3$), specific energy consumption (kWh/kg), throughput (kg/h), space-time yield (STY, kg/m$^3$$\cdot$h) and energy utilization efficiency (kg/kWh). The results show that the nanocomposite system offers the best treatment efficiency (93.17% removal efficiency and very low variability (standard deviation = 0.78%), showing good stability and reliability of the process. We found that nanocomposite system had moderate operating cost of 0.109−0.116 USD/m$^3$ and specific energy consumption of 3.60−6.52 kWh/kg, with an average value of 4.70 kWh/kg. Also, it has the highest STY (0.94 kg/m$^3$$\cdot$h) and high energy utilization efficiency (0.2776 kg/kWh). In contrast, the FB system has the lowest average operating cost (0.1016 USD/m$^3$), lowest average specific energy consumption (4.20 kWh/kg) and the best energy utilization efficiency (0.2493 kg/kWh) and is the most economical option even with the lowest pollutant removal efficiency. The EC system provided the best removal efficiency (91.32%), but the highest operating cost (0.1242 USD/m$^3$) and energy consumption (6.50 kWh/kg) of the other technologies. In analysis of variance (ANOVA) and Tukey’s Honestly Significant Difference (HSD) tests, there was significant difference between all the technologies ($p$ $<$ 0.05). The nanocomposite system achieved 5.39% removal efficiency and the FB system was able to have better energy utilization than the EC and nanocomposite technology. In general, the nanocomposite technology was the best in terms of treatment efficiency, energy efficiency, and operational cost optimization and the FB system is the best choice for large-scale applications.

The expansion of the shipbuilding industry in coastal areas contributes substantially to economic development while simultaneously posing significant risks to air quality and community health. This study analyzed the concentration and spatial distribution of air pollutants and assessed non-carcinogenic health risks in the shipyard industrial area of Batam City. Air quality measurements were conducted for PM$_{2.5}$, SO$_2$, NO$_2$, CO, and Pb parameters at multiple receptor points at different distances from the emission source. The field measurement data were then integrated with dispersion modeling using the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) based on local meteorological conditions. Health risk was evaluated using the Hazard Quotient (HQ) approach with reference to national air quality standards. Pollutant concentrations decreased consistently with increasing distance from emission sources, with PM$_{2.5}$ exhibiting the widest and most persistent spatial distribution. Although most pollutant concentrations remained below regulatory thresholds, PM$_{2.5}$ yielded HQ values exceeding 1.0 across all receptor distances up to 2000 m, indicating significant non-carcinogenic health risk at all observed distances. Model validation demonstrated strong spatial agreement between measured and simulated concentrations ($R^2$ $>$ 0.84), with a consistent tendency toward underestimation of absolute values. The integration of spatial dispersion modeling with health risk assessment offers a comprehensive analytical framework for air quality management and public health protection in coastal industrial settings.

Shoreline change strongly affects ecosystem conditions and livelihood security in deltaic coasts. However, long-term and spatially explicit baselines remain limited for many rapidly changing coastal systems. This study quantified multi-decadal shoreline dynamics in Ujung Pangkah, Indonesia, to identify persistent patterns of accretion and erosion and to assess their implications for sustainable coastal management. Multi-epoch satellite images from 1973 to 2021 were used to extract shoreline positions through water-index-based classification. The extracted shorelines were analyzed using standardized Digital Shoreline Analysis System (DSAS) metrics to estimate net shoreline movement (NSM) and end point rate (EPR) across segmented coastal areas. The results indicate a segment-structured shoreline mosaic rather than a uniform coast-wide trend. Most sectors were accretion-dominated, with the accretion component reaching approximately +12 to +15 m$\cdot$yr$^{-1}$, particularly in Area C. In contrast, Area D formed the main erosional hotspot, with an erosion component of -8.68 m$\cdot$yr$^{-1}$ and an NSM erosion value of -416.53 m, while its net EPR and net NSM were -0.66 m$\cdot$yr$^{-1}$ and -31.77 m, respectively. These findings show that shoreline change in Ujung Pangkah is spatially concentrated in localized reaches. Therefore, coast-wide averages may obscure areas where erosion risk persists and where accretion gains are sustained. This study provides a quantitative long-term baseline and a reproducible remote-sensing and GIS-based workflow to support hotspot identification, segment-scale monitoring, and the prioritization of coastal protection and rehabilitation measures in dynamic deltaic environments.

This study aims to develop a model for predicting daily sea wave heights in the Makassar Strait to support shipping safety in tropical waters. Observation data were obtained from the Makassar station of the Meteorology, Climatology, and Geophysical Agency (Badan Meteorologi, Klimatologi, dan Geofisika, BMKG) (January 2018–December 2023), covering wind speed, wind direction, sea surface temperature, and rainfall. Feature selection was performed using Frequent Pattern Growth (FP-Growth), which was chosen because it efficiently finds association patterns between variables with only two database scans, making it more economical than other techniques such as Recursive Feature Elimination or Principal Component Analysis. The selected features were used to build a Support Vector Regression (SVR) model optimised with the Fruit Fly Optimisation Algorithm (FOA). The evaluation was conducted with zonal validation in three sub-regions of the Makassar Strait (north, central, south) using a lead time of one day ahead. The results show that the SVR-FOA model produces an average root mean square error (RMSE) of 0.4938 m (95% confidence interval (CI): 0.472–0.516), mean absolute percentage error (MAPE) of 0.00208 (95% CI: 0.00195–0.00221), and a correlation of 0.935. SVR-FOA reduced the RMSE by 16.8% compared to the default SVR, while compared to the grid search SVR, there was a 6.7% reduction. The model’s performance is comparable to similar studies in the literature, although the RMSE is still higher than Long-Short Term Memory (LSTM) and XGBoost; however, SVR-FOA excels in stability between zones. In conclusion, SVR-FOA with FP-Growth feature selection effectively predicts daily sea wave height in the Makassar Strait. Further research is needed to test shorter time scale predictions, real-time data integration, and field validation with stakeholders.

Water scarcity is a major challenge in agriculture, where nearly 70% to 80% of freshwater is used for irrigation. This study reviews clay pot irrigation and its integration with drip irrigation and Internet of Things (IoT)-based monitoring for improving water use efficiency. Previous studies have reported substantial irrigation water savings under clay pot irrigation systems up to 80% compared to conventional methods by supplying water slowly near to the root zone and reducing losses. It also provides good water use efficiency and maintains acceptable crop yield under limited water conditions. Previous studies have shown that the performance of the clay pot irrigation depends on pot design, including size and shape. A few studies suggested that storing water in clay pots and the water passing through the pot walls may lead to some improvement in water quality, although detailed data is still limited. However, the integration with drip irrigation and IoT-based control can further improve water distribution and reduce manual effort.

Environmental noise generated by motorcycle traffic constitutes a critical challenge for intermediate cities of the Global South, where high motorization rates intensify exposure to stressful acoustic environments. In Florencia (Caquetá, Colombia)—a city in which motorcycles represent 96.6% of the vehicle fleet—noise functions not only as an environmental pollutant but also as a psychosocial trigger associated with irritability, stress, and aggressive driving behaviors among young riders. This study evaluates urban motorcyclists’ perceived effectiveness of regulatory measures aimed at noise and traffic control, considering how education level and driving experience shape normative perceptions. Using a non-experimental, cross-sectional design, data were collected from 502 motorcyclists. Kruskal–Wallis tests and Spearman correlations revealed a significant positive association between higher education and favorable perceptions of regulatory effectiveness, while no association was observed for driving experience. An exploratory factor analysis (EFA) confirmed a two-factor structure (54.3% variance), differentiating structural/collective measures from individual/educational ones. Overall, structural and educational interventions were perceived as more effective than coercive approaches. These findings highlight the need for context-sensitive regulatory frameworks that integrate social legitimacy, cultural adaptation, and psychological determinants of behavior. The study contributes empirical evidence for designing participatory and education-centered strategies for noise management and mobility governance in structurally informal urban contexts such as Florencia.

This study investigates the influence of three distinct water sources on the physical and chemical properties of soil in the Al-Hammar Marsh of southern Iraq. Fifteen geo-referenced soil samples were collected from zones affected by (i) Euphrates River freshwater, (ii) saline agricultural drainage from the Al-Khamisiya canal, and (iii) brackish water intrusion from the Aramco feeder channel. Samples were tested for gypsum content, pH, electrical conductivity (EC), organic matter (OM) content, bulk density, porosity, texture, and the concentration of basic ions. Spatial variability was evaluated utilizing geographic information system (GIS)-based interpolation methods. The highest salinity levels (mean EC, (EC) = 2,292 µS/cm) were found in regions influenced by the main outfall drain (MOD), marked by high concentrations of chloride and sulfate, reduced porosity, and heightened soil alkalinity. Conversely, Umm Al-Wudaa, affected by Euphrates freshwater, exhibited superior soil structure, elevated levels of OM (7.86%), and reduced salinity (EC = 1,960 $\mu$S/cm), signifying efficient natural leaching. Areas supplied by Aramco exhibited the presence of gypsum and marine ions, along with an intermediate salinity (EC = 2265 $\mu$S/cm). The places with the highest salinity were detected, and the dilution of salt downstream was confirmed via GIS analysis. The findings highlight the need for integrated salinity management in Al-Hammar Marsh through controlled freshwater releases, targeted soil amendments, wetland-based pretreatment of drainage inflows, and continuous GIS-supported monitoring.