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The difference between physical activations (by sonications) and chemical activations (by ammonia) on sawdust biochar has been investigated in this study by comparing the removal of Cu(II) ions from an aqueous medium by adsorption on sawdust biochar (SD), sonicated sawdust biochar (SSD), and ammonia-modified sawdust biochar (SDA) with stirring at room temperature, pH value of 5.5–6.0, and 200 rpm. The biochar was prepared by the dehydrations of wood sawdust by reflux with sulfuric acid, and the biochar formed has been activated physically by sonications and chemically by ammonia solutions and then characterized by the Fourier transform infrared (FTIR); Brunauer, Emmett, and Teller (BET); scanning electron microscope (SEM); thermal gravimetric analysis (TGA); and energy-dispersive spectroscopy (EDX) analyses. The removal of Cu(II) ions involves 100 mL of sample volume and initial Cu(II) ion concentrations (conc) 50, 75, 100, 125, 150, 175, and 200 mg L−1 and the biochar doses of 100, 150, 200, 250, and 300 mg. The maximum removal percentage of Cu(II) ions was 95.56, 96.67, and 98.33% for SD, SSD, and SDA biochars, respectively, for 50 mg L−1 Cu(II) ion initial conc and 1.0 g L−1 adsorbent dose. The correlation coefficient (R2) was used to confirm the data obtained from the isotherm models. The Langmuir isotherm model was best fitted to the experimental data of SD, SSD, and SDA. The maximum adsorption capacities (Qm) of SD, SSD, and SDA are 91.74, 112.36, and 133.33 mg g−1, respectively. The degree of fitting using the non-linear isotherm models was in the sequence of Langmuir (LNR) (ideal fit) > Freundlich (FRH) > Temkin (SD and SSD) and FRH (ideal fit) > LNR > Temkin (SDA). LNR and FRH ideally described the biosorption of Cu(II) ions to SD and SSD and SDA owing to the low values of χ2 and R2 obtained using the non-linear isotherm models. The adsorption rate was well-ordered by the pseudo-second-order (PSO) rate models. Finally, chemically modified biochar with ammonia solutions (SDA) enhances the Cu(II) ions’ adsorption efficiency more than physical activations by sonications (SSD). Response surface methodology (RSM) optimization analysis was studied for the removal of Cu(II) ions using SD, SSD, and SDA biochars.
The difference between physical activations (by sonications) and chemical activations (by ammonia) on sawdust biochar has been investigated in this study by comparing the removal of Cu(II) ions from an aqueous medium by adsorption on sawdust biochar (SD), sonicated sawdust biochar (SSD), and ammonia-modified sawdust biochar (SDA) with stirring at room temperature, pH value of 5.5–6.0, and 200 rpm. The biochar was prepared by the dehydrations of wood sawdust by reflux with sulfuric acid, and the biochar formed has been activated physically by sonications and chemically by ammonia solutions and then characterized by the Fourier transform infrared (FTIR); Brunauer, Emmett, and Teller (BET); scanning electron microscope (SEM); thermal gravimetric analysis (TGA); and energy-dispersive spectroscopy (EDX) analyses. The removal of Cu(II) ions involves 100 mL of sample volume and initial Cu(II) ion concentrations (conc) 50, 75, 100, 125, 150, 175, and 200 mg L−1 and the biochar doses of 100, 150, 200, 250, and 300 mg. The maximum removal percentage of Cu(II) ions was 95.56, 96.67, and 98.33% for SD, SSD, and SDA biochars, respectively, for 50 mg L−1 Cu(II) ion initial conc and 1.0 g L−1 adsorbent dose. The correlation coefficient (R2) was used to confirm the data obtained from the isotherm models. The Langmuir isotherm model was best fitted to the experimental data of SD, SSD, and SDA. The maximum adsorption capacities (Qm) of SD, SSD, and SDA are 91.74, 112.36, and 133.33 mg g−1, respectively. The degree of fitting using the non-linear isotherm models was in the sequence of Langmuir (LNR) (ideal fit) > Freundlich (FRH) > Temkin (SD and SSD) and FRH (ideal fit) > LNR > Temkin (SDA). LNR and FRH ideally described the biosorption of Cu(II) ions to SD and SSD and SDA owing to the low values of χ2 and R2 obtained using the non-linear isotherm models. The adsorption rate was well-ordered by the pseudo-second-order (PSO) rate models. Finally, chemically modified biochar with ammonia solutions (SDA) enhances the Cu(II) ions’ adsorption efficiency more than physical activations by sonications (SSD). Response surface methodology (RSM) optimization analysis was studied for the removal of Cu(II) ions using SD, SSD, and SDA biochars.
A study was conducted on 31 surface sediments located in different sectors of the Egyptian Mediterranean coast. The sediments were analyzed for their pollution levels of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs). The sediments were collected from various depths in harbors, coastal lakes, bays, and lagoons, covering the southeastern Mediterranean of the Nile Delta region. The study aimed at determining the distribution, origin, and potential ecological impact of OCP and PCB pollutants. The researchers used the SRM method of GC‐MS/MS to measure the concentration of 18 PCBs and 16 OCPs residues. The study found that the total concentration of OCPs in the samples ranged from 3.091 to 20.512 ng/g, with a mean of 8.749 ± 3.677 ng/g. The total concentration of PCB residues ranged from 2.926 to 20.77 ng/g, with a mean of 5.68 ± 3.282 ng/g. The concentration of DDTs exceeded the effect range low (ERL) (1.00) and threshold effect level (TEL) (1.19) in several stations, but it was still below the effect range median (ERM) (7.00) and the probable effect level (PEL) (4.77). This indicates a low ecological risk. The principal component analysis (PCA) was also conducted to determine the sources of all pollutants in the sediment. The PCA showed significant correlations between the concentrations of Gama‐HCH and Beta‐HCH (0.741), suggesting similar sources.Practitioner Points OCPs and PCBs residues were analyzed in the sediment of the southeastern Mediterranean. The concentration, existence, and causes of OCPs and PCBs were investigated. OCPs and PCBs ecological risk and ecotoxicological calculation were investigated in detail. Cluster analysis, PCA, and correlation coefficient were also investigated.
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