International Journal of Environmental Science and Technology

The aim of this study was to compare the performance of support vector machine and artificial neural network techniques to predict the soil cation exchange capacity of an agricultural research station in terms of soil characteristics (clay, silt, sand, gypsum, organic matter). The data consist of 380 soil samples collected from different horizons of 80 soil profiles located in the Khoja (Khajeh) region of Azerbaijani provinces, Iran. The support vector machine and artificial neural network models predict the cation exchange capacity from the above soil characteristics of the samples. The models’ results are compared using three criteria, i.e., root-mean-square errors, Nash–Sutcliffe and the correlation coefficient. A comparison of support vector machine results with artificial neural network method indicates that artificial neural network is better than the support vector machine method in prediction of the cation exchange capacity.

The performance of boehmite granules in the adsorption of thorium ions from aqueous solution was studied in batch and continuous processes. The effective parameters such as pH and temperature were evaluated. First, the pH value was optimized, and then, several experiments were carried out in the optimized pH value and various temperatures (30, 40 and 55 °C). The equilibrium adsorption data were analyzed using Weber–van Vliet, Fritz–Schlunder and Hill isotherm models to find the best-fit isotherm. Thermodynamic parameters including standard enthalpy change (∆H°), standard entropy change (ΔS°) and standard Gibbs free energy change (∆G°) were determined in the batch mode. The application of adsorbent for thorium removal from aqueous solutions was examined through column breakthrough studies. The effect of various parameters such as inlet concentration, flow rate and bed height on the breakthrough curves was investigated. The maximum column capacity was found to be about 57.78 mg thorium per gram of boehmite. Three widely used models, including Thomas, Adams–Bohart and Bed Depth Service Time (BDST), were used to fit the data obtained from the experiments. The results indicated that the Thomas model could describe the column dynamics in all operating situations. But, the Adams–Bohart model can only estimate the primary section of the breakthrough curves.

Wise prediction of discharge is vital for effective utilization of water resources and hydropower generation. There is no doubt that the future climate change will substantially affect precipitation amount, discharge and hydro-meteorology which are considered as major sources for hydropower energy. In this study, we present an application of downscaling technique on prediction of discharge of a local gage station in Turkey. Variable input set of the Third Generation Coupled Global Climate Model variables are statistically downscaled and coupled with variable forms of local discharge (Q, LnQ, MavQ, StdQ, and Q/Qmax) by using five different models as Gene-expression programming, Group Method of Data Handling, K-nearest neighbour, logistic regression and linear regression. Different sub-models are developed and calibrated based on the training period, and the best model is selected based on the testing period. Future projecting of discharge is done based on the Third Generation Coupled Global Climate Model A2 scenario, and the financial analysis is carried out by using this forecasted Q values.

Simultaneous investigation of kerf quality and fume emissions during laser cutting process is essential to achieve efficient and hygienic cutting process. In this study, an experimental investigation on process factors of CO2 laser cutting (CLC) of polyvinyl chloride (PVC) sheets has been carried out followed by an optimization study to enhance the kerf geometry and reduce the emitted harmful fumes. Laser power, transverse cutting speed, and sheet thickness have been considered as the process factors. The kerf geometry has been measured using optical microscope. The emitted fumes are considered to be proportional to the generated kerf volume. An L9 experimental design plan was designed using Taguchi method, and the results were evaluated using analysis of variance (ANOVA). Second-order regression models have been developed to correlate the input parameters to the process responses. Genetic algorithm (GA) was implemented to obtain the optimum process parameters that should be used to minimize the kerf volume and consequently the emitted fumes. A significant decrease in kerf volume has been obtained using the optimum process parameters. Confirmation experiments have been conducted to verify the accuracy of the proposed approach. The proposed optimization method shows a considerable decrease in the kerf volume and the emitted fumes, especially for large sheet thicknesses. The emitted fumes can be reduced by about 39% and 61% for sheet thickness of 7 mm and 10 mm, respectively, by applying the optimum cutting parameters obtained from this study.

This study aims to gain a better understanding of how climatic and anthropogenic factors have affected the Aral Sea Area (ASA). The dynamic changes of the ASA from 1920 to 2020 were investigated, and then the relationship between the water area and its drivers was evaluated using different regression analyses. The results demonstrate that the water surface area exhibited minor fluctuations with an upward trend of 0.04 km2/year from 1920 to 1960. Since then, the water area decreased dramatically. It reduced by 88% in 2020 compared to that in 1920, and the decreasing rate slowed significantly after 2010. The multi-factor analysis determined that human activities are the root cause of declining in water area. The multiple stepwise regression results suggest that the water withdrawals from rivers (dam capacity > domestic water consumption > agricultural water consumption) could be responsible for most of the variation in ASA, and 82% of total withdrawal water was used for agricultural consumption. The diversion of rivers (for irrigation) that historically fed the Aral Sea, resulting in a decrease in inflow entering the sea, is another cause of the ASA's shrinkage.

The present work aimed to study the overall perception, awareness, practice, and willingness on solid waste management of the residents of an industrial district in India. The results suggest that the awareness regarding SWM was mostly impacted by the region of inhabitant with urban dwellers remaining aware about the ill-effects of solid waste (88.67%), the timing of garbage cleaning (53.91%), and regularity in solid waste cleaning (68.36%) than their counterparts in the rural sector with 79.91, 34.11, and 45.79%, respectively. The practice, however, showed variability with gender determining the use of separate bins (51.87% males and 67.69% females), region determining burning of plastic (17.97% urban and 37.85% rural), gender (56.44% males and 75.11% females), and region (73.44% urban and 56.07% rural) determining the use of community bins and education (OR = 2.917; CI = 1.258 – 6.764) for reuse of wastes. Similarly, willingness also showed variability in results with gender determining the use of paper bags (85.06% males and 94.76% females), occupation determining the community participation (OR = 3.557, CI = 1.162 – 10.890), gender determining the installation of composting plant (75.93% males and 82.28% females) and age determining the financial contribution (OR = 1.995, CI = 1.039 – 3.831). The result obtained suggests that the overall public perception of solid waste among the residents is not satisfactory. In addition, the government should take steps to engage the general people in addressing local and regional solid waste challenges.

A hydroponic experiment has been conducted for desalination of saline water by culturing Ipomoea aquatica, Alternanthera philoxeroides and Ludwigia adscendens at 0–7 dS m−1 salinity level. Water samples were collected at 15-day interval, and the plants were harvested after 45 days. They were separated into root, stem and leaf. EC value decreased in water with increasing time for all halophytes. Root and leaf contain higher amounts of sodium as compared with those of stem. The highest accumulation of sodium was found in the root of A. philoxeroides (145.63 g kg−1); however, I. aquatic has high phytodesalination capacity (130 kg Na+ ha−1) due to high productivity than A. philoxeroides (105 kg Na+ ha−1) and L. adscendens (80 kg Na+ ha−1). Bio-concentration factors (56.10–80.29) and translocation factor values (˃ 1) indicated that these halophytes were good sodium accumulator. Sodium adsorption ration values lied between 16.8–18 at 3 dS m−1 and 20–25.5 at 5 and 7 dS m−1 showed that these halophytes improved the water quality for irrigation. Anatomical variation from microscopic cellular images illustrated that spongy mesophyll cells along with sub-stomatal cells in leaf and xylem vessels along with vacuolar sequestration might be responsible for Na accumulation in the stem of these halophytes.

Over the past few decades, the world is facing critical water supply problems caused by the relentless increase of global human populations and the associated rise of anthropogenic activities. Heavy metals are among the main water pollutants which pose a great threat to human health. Hence, globally there has been a large amount of research devoted to investigating cost-effective and sustainable methods for removal of heavy metals from polluted water. One such area of interest is the utilization of construction and demolition waste (CDW) materials for the adsorptive removal of heavy metal ions (As, Cd, Co, Cr, Cu, Hg, Ni, Pb, Sr, and Zn). This review focuses on the most current research for the use of CDW as an adsorbent. The different heavy metal ion removal mechanisms involved are also discussed. Further, this article documents the regeneration and reuse strategies for heavy metal treated adsorbents and the efforts to apply these materials in large-scale applications. Finally, the main research gaps are identified and future research directions suggested.

In this paper, principal component analysis (PCA) and positive matrix factorization (PMF) multivariate statistical techniques were applied to evaluate the spatiotemporal variation in 13 conventional water quality parameters (TOC, TN, NO2−, NO3−, TP, SO4−2, Cl−, TSS, color, pH, temperature, DO, EC) at eight monitoring stations for a duration of a year (2016–2017) in the upstream parts of the Ergene basin (NW Turkey). The eight monitoring stations were divided into two groups (five sites for Gr-A and three sites for Gr-B) considering pollution levels of the parameters and point/non-point sources determined by field observations. The principal component analysis defined four and three latent factors explaining 87% and 89% of the total variance in Gr-A and Gr-B datasets, respectively. Component numbers defined in PCA were manually assigned to the positive matrix factorization model. PCA was seen to be an important index for defining the number of factors causing high uncertainty for PMF. The factors derived from the PMF model revealed that the dominant pollutant sources for Gr-A sites are textile and leather industry discharges, agricultural activities, domestic discharges and seasonal factors. Gr-B sites are defined as domestic discharges, agricultural fertilizers and industrial discharges. Therefore, PMF analysis for conventional water quality parameters is a consistent statistical technique for the identification of complex pollution sources.