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Preparation of new sorbent from precipitation of nano-sized (Mg/Fe-CTAB)- layered double hydroxide (LDH) on the surfaces of sewage sludge byproduct to remove the anionic and cationic dyes was the focal point of this work. The presence of nanoparticles and enlarged of interlayers by CTAB intercalation have increased the sludge surface area from 5.34 to 10.32 m2/g. The CTAB mass 0.03 g/50 mL, sludge dosage 1 g/50 mL and (Mg/Fe) molar ratio 2 were the best preparation conditions required to obtain effective sorbent with efficiencies exceeded 93% for MB and CR dyes. These efficiencies were obtained under operational conditions for batch study of 0.5 g coated sludge per 50 mL colored dye solution, initial pH 3 (for CR) and 12 (for MB), and time 3 h for 10 mg/L dyes at 200 rpm. Models of Langmuir and pseudo second-order have a high capability in the representation of sorption records with maximum capacities of adsorption 163.6 and 132.6 mg/g for CR and MB dye, respectively. The X-ray diffraction analysis proved that the calcite occurred mainly at 2θ = 29.8° while quartz corresponded to the 21, 26.6, 36.4, 36.9, 50.1, 60.01 and 68.4°. Characterization tests showed that nano-sized particles of magnesium/iron were precipitated on the sludge due to the formation of hydrotalcite-like compounds with an increase in the percentages of Mg and Fe from 0.87 and 1.36 to 4.25 and 3.03%, respectively. The results showed that the electrostatic attraction, intra-particle diffusion and hydrogen bonding were predominant mechanisms for removal of CR and MB onto coated sludge. [Display omitted] • Using sewage sludge byproduct for manufacturing novel sorbent. • (Mg/Fe-CTAB)-layered double hydroxide (LDH) nanoparticles were prepared. • Nanoparticles precipitate onto sludge to remove CR and MB dyes. • Maximum adsorption capacities were 163.6 & 132.6 mg/g for CR and MB, respectively. • Removal of dyes were achieved by electrostatic interaction and hydrogen bonding. [ABSTRACT FROM AUTHOR]

Estrogenic hormones, found as micropollutants in water systems, give rise to grave concerns for human health and marine ecosystems, triggering a cascade of adverse effects. This research presents an innovative manufacturing approach using nanoscale layered double hydroxides of magnesium and iron, with sodium dodecyl sulfate surfactant, to create highly efficient sorbent cement kiln dust (CKD) based beads (CKD/MgFe-SDS-LDH-beads). These beads effectively remove estrone from water. Optimization of the preparation process considered factors like molar Mg/Fe ratio, CKD dosage, pH, and SDS dosage using Response Surface Methodology (RSM). The adsorption process was well-characterized by Langmuir isotherm and pseudo-second-order kinetic models, demonstrating a remarkable 6.491 mg/g sorption capacity. Results proved that the calcite was the main component of the CKD with miners of dolomite, and quartz. Adsorption capacity, surface charges, and the availability of vacant sites may be the main mechanisms responsible of removal process. Experimental tests confirmed the beads' potential for estrone removal, aligning with the Bohart-Adams and Thomas-BDST models. This study introduces a promising, eco-friendly solution for addressing water contamination challenges. [Display omitted] • Using cement kiln dust byproducts for preparation of novel sorbent. • Sorbent is prepared in the form of sodium alginate beads. • Beads are composed of kiln dust, magnesium, iron and surfactant. • Beads are used to treat water laden with estrone. • Maximum adsorption capacity for prepared beads is 6.491 mg/g. [ABSTRACT FROM AUTHOR]

[Display omitted] • High-resolution prediction of air fine particular matter concentration is conducted. • New developed machine learning algorithms are adopted for this purpose. • Prediction for temporal/spatial variability of PM 2.5 over Iraq region is studied. • Research provided good forecasting spatial variability of PM 2.5 at high resolution. This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM 2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM 2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM 2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM 2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM 2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM 2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM 2.5 forecasting maps. [ABSTRACT FROM AUTHOR]

Documenting habitats of rangeland plant species is required to properly manage rangelands and to understand ecosystem processes. A reliable rangeland species potential map can help managers and policy makers design a sustainable grazing system on rangelands. The aim of this study is to map the plant species in the Qurveh City rangelands, Kurdistan Province, Iran, using state-of-the-art machine learning algorithms, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes (NB), Bayes Net (BN) and Classification and Regression Tree (CART). A total of 185 rangeland species were used in the study, together with 20 conditioning factors, to build and validate models. The One-R feature section technique and multicollinearity test were used, respectively, to determine the most important factors and correlations between them. Model validation was performed using sensitivity, specificity, accuracy, F1-measure, Matthews correlation coefficient (MCC), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). Results showed that topographic wetness index (TWI), slope angle, elevation, soil phosphorus and soil potassium were the five most important factors to increase the rangeland plants habitat suitability. The Naïve Bayes algorithm (AUC = 0.782) had the highest performance and prediction accuracy and best consistency across the species in the investigated rangeland, followed by the SVM (AUC = 0.763), ANN (AUC = 0.762), CART (AUC = 0.627), and BN (AUC = 0.617) models. [Display omitted] • The habitats of important ecological rangeland plants were modeled and mapped. • Machine learning algorithms are robust tools in rangeland rehabilitation and management. • Topographic, phosphorus and potassium were the main factors to increase habitat suitability. • NB and CART had the highest and lowest prediction for studied species of investigated rangeland. • High potential rangeland habitats can help decision makers in better rangeland management. [ABSTRACT FROM AUTHOR]