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Quantifying total carbon (TC) stocks in soil across various mangrove ecosystems is key to understanding the global carbon cycle to reduce greenhouse gas emissions. Estimating mangrove TC at a large scale remains challenging due to the difficulty and high cost of soil carbon measurements when the number of samples is high. In the present study, we investigated the capability of Sentinel-2 multispectral data together with a state-of-the-art machine learning (ML) technique, which is a combination of CatBoost regression (CBR) and a genetic algorithm (GA) for feature selection and optimization (the CBR-GA model) to estimate the mangrove soil C stocks across the mangrove ecosystems in North Vietnam. We used the field survey data collected from 177 soil cores. We compared the performance of the proposed model with those of the four ML algorithms, i.e., the extreme gradient boosting regression (XGBR), the light gradient boosting machine regression (LGBMR), the support vector regression (SVR), and the random forest regression (RFR) models. Our proposed model estimated the TC level in the soil as 35.06–166.83 Mg ha−1 (average = 92.27 Mg ha−1) with satisfactory accuracy (R2 = 0.665, RMSE = 18.41 Mg ha−1) and yielded the best prediction performance among all the ML techniques. We conclude that the Sentinel-2 data combined with the CBR-GA model can improve estimates of the mangrove TC at 10 m spatial resolution in tropical areas. The effectiveness of the proposed approach should be further evaluated for different mangrove soils of the other mangrove ecosystems in tropical and semi-tropical regions.

Adsorption characteristics of the antibiotic meropenem on a novel magnetic material synthesized by surface coating cobalt iron oxide (CFO) with gold nanoparticles (AuNPs) were systematically investigated. The AuNPs can enhance material adsorption capacity by having high affinity towards the thioether and amine groups in the meropenem structure. Au coverage on the CFO surface decreased the saturation magnetization from 55.8 emu/g to 48.8 emu/g, still allowing synthesized CFO@Au nanomaterials to be magnetically recoverable. The CFO@Au nanomaterials showed enhanced adsorption capacity of 25.5 mg/g at optimum conditions of pH 4.0 adsorption time 120 min, and adsorbent mass 0.05 g. Adsorption equilibrium was in accordance with a monolayer Langmuir isotherm, while the adsorption kinetics followed pseudo-first-order kinetics and intraparticle diffusion models. This work provides a simple method to prepare a magnetic composite material with high adsorption efficiency for meropenem and probably other thioether-containing substances.

The objective of the present study is to investigate removal of cationic dye, rhodamine B (RhB), in water environment using a high-performance absorbent based on metal oxide nanomaterials toward green chemistry. The adsorption of sodium dodecyl sulfate (SDS) onto synthesized alpha alumina (α-Al2O3) material (M0) at different ionic strengths under low pH was studied to fabricate a new adsorbent as SDS-modified α-Al2O3 material (M1). The RhB removal using M1 was much higher than M0 under the same experimental conditions. The optimal conditions for RhB removal using M1 were found to be contact time 30 min, pH 4, and adsorbent dosage 5 mg/mL. The maximum RhB removal using M1 achieved 100%, and adsorption amount reached 52.0 mg/g. Adsorption isotherms of RhB onto M1 were well fitted by the two-step adsorption model. The electrostatic attraction between positive RhB molecules and negatively charged M1 surface controlled the adsorption that was evaluated by the surface charge change with zeta potential and adsorption isotherms. Very high RhB removal of greater than 98% after four regenerations of M1 and the maximum removal for all actual textile wastewater samples demonstrate that SDS-modified nano α-Al2O3 is a high-performance and reusable material for RhB removal from wastewater.

Concentrations of As and other trace elements were measured in groundwater, rice, hair, urine, and blood samples of people consuming As-contaminated groundwater in a village of Ha Nam province, northern Vietnam to understand the recent status of contamination and assess the possible risks of human exposure. Elevated concentrations of As in groundwater were still observed, exceeding the WHO guideline value in most of the tube wells investigated. Significant positive correlations between As concentrations in groundwater and human samples (hair and urine) were observed. Arsenic concentrations in human and hair appeared to be related to the groundwater usage habit, with higher levels found in drinking group than those in the washing group. Significant good correlations were also encountered between cumulative intakes of As, Mn, and Ba through groundwater consumption and hair concentrations. All these results indicate the chronic exposure to As and some other elements such as Mn and Ba. The total intakes of As, Mn, and Ba through rice and groundwater consumption were estimated to be ranged from 80–836, 49.3–1850, and 311–97100 µg/day, respectively. The daily intakes of As of the study area ranged from 1.6–16.7 µg/kg body wt./day, mean: 7.15 µg/kg body wt./day, in which about 85% of the subjects were above the provisional tolerable daily intake proposed by WHO.

This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R2) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R2 = 0.683, RMSE = 25.08 Mg·ha−1) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha−1 to 142 Mg·ha−1 (with an average of 72.47 Mg·ha−1). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics.

In this paper, we reported a new, simple, accurate, and precise extractive spectrophotometric method for the determination of fluoroquinolones (FQs) including ciprofloxacin (CFX), levofloxacin (LFX), and ofloxacin (OFX) in pharmaceutical formulations. The proposed method is based on the ion-pair formation complexes between FQs and an anionic dye, bromothymol blue (BTB), in acidic medium. The yellow-colored complexes which were extracted into chloroform were measured at the wavelengths of 420, 415, and 418 nm for CFX, LFX, and OFX, respectively. Some effective conditions such as pH, dye concentration, shaking time, and organic solvents were also systematically studied. Very good limit of detection (LOD) of 0.084 µg/mL, 0.101 µg/mL, and 0.105 µg/mL were found for CFX, LFX, and OFX, respectively. The stoichiometry of the complexes formed between FQs and BTB determined by Job’s method of continuous variation was 1 : 1. No interference was observed from common excipients occurred in pharmaceutical formulations. The proposed method has been successfully applied to determine the FQs in some pharmaceutical products. A good agreement between extractive spectrophotometric method with high-performance liquid chromatography mass spectrometry (HPLC-MS) for the determination of FQs in some real samples demonstrates that the proposed method is suitable to quantify FQs in pharmaceutical formulations.

We investigated adsorption of anionic surfactants, sodium dodecyl sulfate (SDS) and sodium tetradecyl sulfate (STS), onto alumina (Al2O3) with large size in the present study. The effective conditions for SDS and STS adsorption onto Al2O3 were systematically studied. The conditions for SDS and STS adsorption onto γ-Al2O3 were optimized and found to be contact time 180 min, pH 4, and 1 mM NaCl. Adsorption of both SDS and STS onto large Al2O3 beads increased with an increase of ionic strength, demonstrating that the adsorption is controlled by electrostatic attraction between anionic sulfate groups and positively charged Al2O3 surface, as well as hydrophobic interactions between long alkyl chains of surfactant molecules. Nevertheless, the hydrophobic interaction in terms of STS adsorption is much higher than that of SDS adsorption. The obtained SDS and STS adsorption isotherms in different NaCl concentrations onto Al2O3 beads were fitted well by two-step adsorption. Adsorption mechanisms were disused in detail on the basis of adsorption isotherm, the change in surface charge, and the change in functional surface groups by Fourier-transform infrared spectroscopy (FTIR). The application of surfactant adsorption onto Al2O3 to remove cadmium ion (Cd2+) was also studied. The optimum conditions for Cd2+ removal using surfactant-modified alumina (SMA) are pH 6, contact time 120 min, and ionic strength 0.1 mM NaCl. Under optimum conditions, the removal efficiency of Cd2+ using SMA increased significantly. We demonstrate that SMA is a novel adsorbent for removal of Cd2+ from aqueous solution.

A high-performance liquid chromatography (HPLC) in combination with inductively coupled plasma mass spectrometry (ICP-MS) as an elemental specific detector was used for the speciation analysis of arsenic compounds in urine and serum samples from Vietnam. Five arsenic species including arsenite (AsIII), arsenate (AsV), monomethylarsonic acid (MMA), dimethylarsinic acid (DMA), and arsenobetaine (AsB) were studied. A gradient elution of ammonium carbonate ((NH4)2CO3), ethylenediaminetetraacetic acid disodium salt (Na2EDTA), and methanol at pH 9.0 utilizing Hamilton PRP-X100 strong anion-exchange column allowed the chromatographic separation of five arsenic species. In this study, urine and serum samples were prepared by dilution in solvent and protein precipitation by trichloroacetic acid, respectively. The extraction efficiency was greater than 91% for urine matrix, and recoveries from spiked samples were in the range of 94–139% for the arsenic species in human serum. The method limit of detection (MDL) and limit of quantification (MQL), which were calculated by signal to noise ratio, were found to be 0.3–1.5 and 1.0–5.0 ng·mL−1, respectively. The concentration of arsenic species in 17 pairs of urine and serum samples from Vietnam was also quantified and evaluated. The major species of arsenic in the urine and serum samples were AsB and DMA.

Removal of copper ion (Cu2+) by using surfactant modified laterite (SML) was investigated in the present study. Characterizations of laterite were examined by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), inductively coupled plasma mass spectrometry (ICP-MS), and total carbon analysis. The optimum conditions for removal of Cu2+ by adsorption using SML were systematically studied and found as pH 6, contact time 90 min, adsorbent dosage 5 mg/mL, and ionic strength 10 mM NaCl. The equilibrium concentration of copper ions was measured by flame atomic absorption spectrometry (F-AAS). Surface modification of laterite by anionic surfactant sodium dodecyl sulfate (SDS) induced a significant increase of the removal efficiency of Cu2+. The surface modifications of laterite by preadsorption of SDS and sequential adsorption of Cu2+ were also evaluated by XRD and FT-IR. The adsorption of Cu2+ onto SML increases with increasing NaCl concentration from 1 to 10 mM, but at high salt concentration this trend is reversed because desorption of SDS from laterite surface was enhanced by increasing salt concentration. Experimental results of Cu2+/SML adsorption isotherms at different ionic strengths can be represented well by a two-step adsorption model. Based on adsorption isotherms, surface charge effects, and surface modification, we suggest that the adsorption mechanism of Cu2+ onto SML was induced by electrostatic attraction between Cu2+ and the negatively charged SML surface and nonelectrostatic interactions between Cu2+ and organic substances in the laterite.

The adsorptive removal of ammonium ions (NH4+) from aqueous solution using surfactant-modified alumina (SMA) was investigated. The optimum NH4+ adsorption removal conditions on SMA were systematically studied and found to be pH 4, contact time 180 min, adsorbent dosage 30 mg mL-1 and ionic strength 1 mM NaCl. The equilibrium concentration of NH4+ was measured by capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-C4D) and spectrophotometry. Surface modification of α-Al2O3 with the anionic surfactant sodium dodecyl sulfate (SDS) at high salt concentration induced a significant increase of removal efficiency. The change in surface charge and surface modification of α-Al2O3 by pre-adsorption of SDS and subsequent adsorption of NH4+ were evaluated by zeta potential measurements and Fourier-transform infrared spectroscopy. Under optimum adsorption conditions, NH4+ removal from two agricultural water samples achieved very high removal efficiencies of 99.5 and 96.5%. The adsorption of NH4+ onto SMA increases with decreasing NaCl concentration because desorption of SDS from the α-Al2O3 surface is minimised. Experimental results of NH4+-SMA adsorption isotherms at different ionic strengths can be represented well by a two-step adsorption model. Based on adsorption isotherms, surface charge effect and surface modification, we suggest that the adsorption mechanism of NH4+ onto SMA was mainly electrostatic attraction between cationic NH4+ and the negatively charged SMA surface. [ABSTRACT FROM AUTHOR]