International Journal of Environmental Science and Technology最新文献

This paper simulated hexavalent chromium (Cr(VI)) adsorption using cocoa pod husk biosorbent in a fixed bed column using Aspen Adsorption. This study was designed to show the effectiveness of computational methods in designing, optimising and evaluating the scaled-up adsorption process using low-cost adsorbents. To the best of our knowledge, the economic analysis of Cr(VI) removal using biosorbent adsorption columns with the assistance of Aspen Adsorption and response-surface methodology (RSM) has not been performed previously. Design Expert and RSM were used to optimise and describe the effect of flow rate and initial concentration on breakthrough and saturation times. The breakthrough time was improved by a higher bed height (2.0 m), a wider diameter (2.0 m), and lowering the flow rate (0.010 L/s). The initial concentration had no effect (1.00 mol/L). The predicted breakthrough and saturation time were 29,360 s and 313,351 s, respectively. Two scenarios were economically compared over 20 years. Scenario 1 (1-day breakthrough time) costs $746,585 and Scenario 2 (4-week breakthrough time) costs $1,538,319. This is because Scenario 2 used a taller, wider column which required a greater amount of adsorbent, and 387,873 m³ of water were processed, respectively. Processed water was dependent on the flow rate and breakthrough time. It was concluded that cocoa pod husk could be an efficient adsorbent and the adsorption process can be successfully simulated and optimised. The use of alternative low-cost adsorbents should be encouraged. The economic study showed that simulation and RSM data could successfully be used for economic analysis.

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Due to different climatic conditions and the high cost of devices, monitoring plant growth is complex and costly. Remote sensing of agricultural lands on a large scale using satellite imagery is more appropriate than hardware equipment such as sensors. One of the main challenges in plant growth is the rate of plant evapotranspiration discussed in the FAO-56 paper. The rate of plant evapotranspiration in the initial, development, and late growth stages is an important issue. Factors such as irrigation method, surface albedo, height above sea level, aerodynamic conditions, leaf and stomata, and soil texture are main to review plant growth from placement to harvest. In this research using Landsat 8 satellite imagery and vegetation indices, we present an approach in which exist several important indicators for sensing plant growth, determining soil texture and moisture, land slope, and improving cloud effects. The water required by the plant at different stages of growth and the equations in the FAO-56 paper estimate and improve using these indices. We have used cubic spline interpolation, Pearson correlation coefficient, explanation coefficient (R²), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and the Mean Standard Error (MSE) to evaluate and compare the accuracy of each index. Finally, the correlation equation of each index is calculated with its actual values. We have also used hypothesis and regression analysis testing to prove efficiency and predict crop behavior.

The results show that the proposed approach by evaluating spatial and temporal data in different climates leads to greater efficiency and crop. The study can consider important parameters in precision agriculture to achieve efficiency, productivity, quality, profitability, and sustainability of crops.

Graphical Abstract

The research is devoted to solve the problem of elevated dust levels in subway air through the implementation of a proposed dust collection system. A comprehensive experiment to determine the fractional and chemical compositions, as well as dust density, in the existing metro systems of Almaty (Kazakhstan) and Novosibirsk (Russian Federation) was conducted. The experiment results led to hypotheses about the sources of dust emission in subways. An innovative method for de-dusting tunnel air has been developed. The method is based on the use of air flows generated by the piston action of trains and the installation of labyrinth filters in the ventilation joints of stations. The parameters of the computational model of a subway line on the basis of decomposition approach to mathematical modeling of aerodynamic processes methods of computational aerodynamics by transition from a full model of a subway line to an open-ended periodic one have been substantiated. The research also justifies the geometric parameters of the labyrinth filters, determining their effectiveness based on air velocity and the number of filter element rows. Additionally, potential energy savings achievable with the proposed system were assessed. The scope of application of the results of the presented study of air distribution from the piston effect in subway structures and the effectiveness of the proposed air filtration system are limited to subways with single-track tunnels and open-type stations equipped with ventilation joints.

Agrochemicals have become essential to meet the increasing demand for food and other commodities, but they can contaminate the environment, especially water resources, if not properly managed. Advanced Oxidation Processes (AOP), such as Fenton’s process, are a quick alternative to remove these toxic compounds from water and wastewater. Previous studies suggest that carboxylic acids can promote the Fenton reaction by accelerating the degradation rate of H₂O₂ and the formation of hydroxyl radicals. In this study, formic and acetic acids were applied in a heterogeneous Fenton system to degrade imidacloprid (C₉H₁₀N₅ClO₂), a model agrochemical molecule. Activated limonite and steel wool were used as low-cost heterogeneous iron precursors. The activated limonite was produced by reducing limonite’s iron under H₂ flow at 200 and 300 °C. The Fenton process with 300 °C-activated limonite showed a reaction rate approximately 8-fold higher than the test using natural limonite and 2-fold higher than the one with limonite activated at 200 °C. Adding acetic acid to the Fenton process using the 300 °C-activated limonite increased the reaction rate by more than 2-fold. When steel wool was used as the iron precursor, the addition of acetic acid resulted in the complete degradation of imidacloprid within 1 min of reaction. Acetic acid exhibited a higher promoting activity than formic acid, and the degradation rate increased with increasing concentrations of both carboxylic acids. This study indicates that carboxylic acids can serve as Fenton promoters to increase the degradation rate of agrochemicals, such as imidacloprid, present in water and wastewater.

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Soil electrokinetic remediation (SEKR) is considered an effective method for removing pollutants by integrating chemical, physical, and biological treatments. It has multiple applications in fields such as dewatering, consolidation, sedimentation, seed germination, etc. This work builds upon a series of recent publications on SEKR, covering topics like electrode approaches, reverse polarity-based SEK, SEK design modifications, installation of perforated materials, and chemical-based SEK. This review focuses on the role of pulsed electric field (PEF) in enhancing the performance of SEKR. There are several other names for the PEF, including periodic, interval, “ON” and “OFF”, intermittent, and breaking electric fields. PEF is proposed as a solution to overcome certain obstacles in SEKR. The review evaluates PEF's impact on (a) remediating organic and inorganic hazards, anions, and salt, (b) integrating with other processes (reverse polarity, phytoremediation, and bioremediation), and (c) electro-dewatering and consolidation. PEF offers several advantages, such as reducing energy consumption, converting the residual fractions into weakly bound fractions, achieving satisfactory remediation, avoiding the voltage drop in the area across the cation exchange membrane, enhancing desorption and/or migration of charged species, permits the exchange of contaminant from solid to the liquid phase (interstitial fluid), allows contaminant diffusion through the soil pores during the off time, generate high electroosmotic flow, avoiding electrode corrosion, decreasing concentration polarization, etc. However, it may also prolong the remediation period and cause contaminant diffusion through the soil pores, which are considered obstacles for SEKR. This review also describe different techniques related to PEF and highlights the potential use of solar cells as a renewable energy source for SEKR.

Graphic abstract

Biomass-based activated carbons are promising as they are effective and low-cost for wastewater remediation. In this study, the removal of lead, copper, and zinc was investigated using activated carbons obtained from two different lichens. The performance of the 5th-order Response Surface methodology (RSM), Machine Learning (ML), and Artificial Neural Network (ANN) model based on Face-Centered Central Composite Design (FCCCD) was evaluated considering initial concentration, temperature, and time effects. The effectiveness of using ANN for accurate prediction in lead and copper removal and the superior performance of ML-based 5th-order RSM for zinc removal were demonstrated. Among the Langmuir, Freundlich, and Temkin isotherm models, the Freundlich model best described the adsorption processes, and the Langmuir maximum adsorption capacities were found to be 105.26 mg/g (Pb/AC-1), 59.52 mg/g (Cu/AC-1), and 53.19 mg/g (Cu/AC-2). Additionally, the pseudo-first-order, pseudo-second-order, and intra-particle diffusion models were examined, and it was found that the adsorption processes followed the pseudo-second-order kinetics and intra-particle diffusion played a significant role. The activation energies and ΔH⁰ values ​​less than 40 kJ/mol and ΔG⁰ values ​​below − 20 kJ/mol showed that the metals were adsorbed by physical mechanisms. The novelty of this study is that the 5th-order RSM model is applied to adsorption processes for the first time, and a multi-faceted approach is used to analyse adsorption processes, including machine learning and ANN, isotherm modeling, thermodynamic evaluation, kinetics analysis, and activation energy calculations.

In-situ water depth measurement is a time-consuming and expensive process for large-scale and frequent monitoring. This underscores the need for alternative methods, such as those based on satellite imagery, which can offer a more efficient and cost-effective solution. This research explores the use of PRISMA hyperspectral images for bathymetry in Nayband Bay, South Iran. The proposed method, SSIP_PSO, includes image pan-sharpening, spectral and spatial information preservation, Particle Swarm Optimization for band ratio selection, and enhanced geometric correction. Among fusion methods tested, the Gram-Schmidt Transform proved most effective for pan-sharpening. The optimal band ratio, b4/b19, improved bathymetry accuracy, reducing root mean square error from 2.291 to 1.716. SSIP_PSO outperformed traditional methods and demonstrated the importance of preserving spatial information in bathymetry.

Chlorination by maintaining the injected chlorine concentration in the range between the minimum and maximum is among the most inexpensive and common disinfection methods in water distribution networks. The minimum concentration of residual chlorine must be observed to control the microbial quality of water. Besides, the maximum chlorine concentration must be observed to control problems related to water smell and taste and to prevent the production of toxic byproducts. This research has developed a model by combining the EPANET model and the ACO_R optimization algorithm to optimize the chlorine injection program during the operation period. According to the results, the ACOR algorithm could be used to derive a suitable program for chlorine injection in the water distribution network such that the permissible constraints of chlorine are observed in the consumption nodes of the network and the consumption of chlorine is reduced to the least level in the network. The developed model was applied to determine an optimal chlorine injection program in a classical example (the Branford network), which was also of interest to some previous researchers. Using the optimal injection program obtained by the model, the chlorine concentration was set at an acceptable network level between the permissible range of 0.2–0.8 g/l. This output was more favorable than the response of other methods in terms of the total residual chlorine concentration, which was 5.8% and 4.7% lower in this method than the methods based on PSO and genetic algorithms, respectively. Moreover, a better convergence speed was obtained in this algorithm, and the number of calculation times of the objective function was 49.5 and 64.4 less than the methods based on PSO and genetic algorithms, respectively. Therefore, the ACO_R algorithm can be used to derive the chlorine injection program to both comply with the permissible constraints of chlorine and reduce chlorine consumption to the minimum level.

This paper utilizes field sampling and laboratory testing to identify potential sources of heavy metals, including rock, dust, rainfall, surface water, and groundwater, employing Pearson Correlation Coefficient—Principal Component—Sources Factors Analysis. A total of 33 surface and 22 profile soil samples were collected to investigate the migration characteristics and spatial distribution of heavy metals in the study area. Additionally, the paper discusses the comparison of soil heavy metal concentrations between sunny and shady slopes. The findings reveal that soil heavy metals in the study area primarily originate from weathering and downward migration deposition of parent rock, indicative of a geological high background area. Moreover, this has led to arsenic contamination in the soil, with an excess rate of 74.5%. It was observed that heavy metals tend to accumulate more on shady slopes than sunny slopes, with mercury and arsenic showing the most significant differences at 69.70% and 49.33% higher levels, respectively. The comprehensive impact index of agricultural soil was calculated at 53.25, indicating a sub-pollution level. Additionally, the study notes that with the increase in crop enrichment coefficient, the variation values of soil heavy metal concentrations decrease, with a correlation coefficient of 0.6077 between the two variables.

Global climate models are important tools for estimating the possible future impacts of climate change and developing necessary adaptation strategies. This study assessed the suitability of global climate models for local climate projections in Hatay, Türkiye. Temperature and precipitation data from different Coupled Model Intercomparison Project Phase 6 climate models were compared with ground-based observations. For stations lacking historical data, multilayer perceptron artificial neural networks were used to generate data. These networks were trained with data from neighboring stations from 1980 to 2014. The most suitable global climate model was determined using a multi-criteria decision-making approach. As a result of the study, it was determined that the multilayer perceptron models effectively generated long-term temperature data with a normalized root mean square error of less than 0.50. Precipitation estimates, while less accurate, achieved reasonable accuracy with a normalized root mean square error of less than 0.70. The evaluation of global climate models revealed a tendency to underestimate minimum temperatures and overestimate maximum temperatures and precipitation. Specifically, the EC-EARTH3, CMCC-ESM2, and MPI-ESM1-2-HR models excelled in maximum temperature estimations; the CMCC-ESM2, GFDL-CM4, and TAIESM1 models were superior for minimum temperatures; and the EC-EARTH3, GFDL-CM4, and MPI-ESM1-2-HR models performed best for precipitation. The findings of this study will provide a framework for the assessment and selection of appropriate climate models for local regions and will help to develop targeted adaptation strategies.