Adsorptive removal of acid red 18 dye from aqueous solution using hexadecyl-trimethyl ammonium chloride modified nano-pumice.

Discharging untreated dye-containing wastewater gives rise to environmental pollution. The present study investigated the removal efficiency and adsorption mechanism of Acid Red 18 (AR18) utilizing hexadecyl-trimethyl ammonium chloride (HDTMA.Cl) modified Nano-pumice (HMNP), which is a novel adsorbent for AR18 removal. The HDTMA.Cl is characterized by XRD, XRF, FESEM, TEM, BET and FTIR analysis. pH, contact time, initial concentration of dye and adsorbent dose were the four different parameters for investigating their effects on the adsorption process. Response surface methodology-central composite design was used to model and improve the study to reduce expenses and the number of experiments. According to the findings, at the ideal conditions (pH = 4.5, sorbent dosage = 2.375 g/l, AR18 concentration = 25 mg/l, and contact time = 70 min), the maximum removal effectiveness was 99%. The Langmuir (R2 = 0.996) and pseudo-second-order (R2 = 0.999) models were obeyed by the adsorption isotherm and kinetic, respectively. The nature of HMNP was discovered to be spontaneous, and thermodynamic investigations revealed that the AR18 adsorption process is endothermic. By tracking the adsorption capacity of the adsorbent for five cycles under ideal conditions, the reusability of HMNP was examined, which showed a reduction in HMNP's adsorption effectiveness from 99 to 85% after five consecutive recycles.

Modeling, optimization and efficient use of MMT K10 nanoclay for Pb (II) removal using RSM, ANN and GA.

Regarding the long-term toxic effects of Pb (II) ions on human health and its bioaccumulation property, taking measures for its reduction in the environment is necessary. The MMT-K10 (montmorillonite-k10) nanoclay was characterized by XRD, XRF, BET, FESEM, and FTIR. The effects of pH, initial concentrations, reaction time, and adsorbent dosage were studied. The experimental design study was carried out with RSM-BBD method. Results prediction and optimization were investigated with RSM and artificial neural network (ANN)-genetic algorithm (GA) respectively. The RSM results showed that the experimental data followed the quadratic model with the highest regression coefficient value (R2 = 0.9903) and insignificant lack of fit (0.2426) showing the validity of the Quadratic model. The optimal adsorption conditions were obtained at pH 5.44, adsorbent = 0.98 g/l, concentration of Pb (II) ions = 25 mg/L, and reaction time = 68 min. Similar optimization results were observed by RSM and artificial neural network-genetic algorithm methods. The experimental data revealed that the process followed the Langmuir isotherm and the maximum adsorption capacity was 40.86 mg/g. Besides, the kinetic data indicated that the results fitted with the pseudo-second-order model. Hence, the MMT-K10 nanoclay can be a suitable adsorbent due to having a natural source, simple and inexpensive preparation, and high adsorption capacity.

Adsorptive removal of humic substances using cationic surfactant-modified nano pumice from water environment: Optimization, isotherm, kinetic and thermodynamic studies.

In this study, nano pumice (NP) and a cationic surfactant (hexadecyltrimethylammonium-chloride (HDTMA.Cl)) treated nano pumice (HMNP) were used for humic acid (HA) adsorption from an aqueous solution. The adsorption process was modeled and optimized using Response surface methodology-central composite design (RSM-CCD) and Artificial neural networks- Genetic algorithm (ANN-GA). The results show that the ANN model outperforms the RSM-CCD model in terms of response prediction. Optimization results based on the RSM-CCD approach proposed pH 3, adsorbent dose 3 g L-1, reaction time 60 min, and initial HA concentration 5 mg L-1 as optimal points of the variables, to reach the maximum adsorption efficiency of 100% and 65.4% by HMNP and NP adsorbents. The maximal adsorption capacity of NP was 1.21 mg g-1, while that of HMNP was 27.34 mg g-1. The optimal points of process parameters by the ANN-GA method are in accordance with the values suggested by the RSM-CCD method. In isotherm studies, Langmuir model was found to be the best-fitted model for both adsorbent with R2 = 0.97 for NP and 0.992 for HMNP, and also among three different kinetic models which were assessed, Pseudo-second-order model with R2 = 0.9989 for HMNP and R2 = 0.9957 for NP were the best-fitted models for HA removal. Thermodynamic studies indicated that the HA adsorption process by both of the adsorbents is endothermic and the nature of HMNP was found spontaneous while for NP was non-spontaneous. The value of ΔH for both adsorbents was in the range of 34-36.8 kJ mol-1 so the process is clarified as chemical-physical adsorption. The reusability test revealed that the adsorption effectiveness of HMNP drops from 100% to 82.4% after 10 consecutive recycles. The influence of interfacing anions indicated that the adsorption efficiency drops from 100% to 95.4% when the anions were added to the reaction solution.

Acid red 18 removal from aqueous solution by nanocrystalline granular ferric hydroxide (GFH); optimization by response surface methodology & genetic-algorithm.

The need for fresh water is more than before by population growth, and industrial development have affected the quality of water supplies, one of the important reason for water contamination is synthetic dyes and their extensive use in industries. Adsorption has been considered as a common methods for dye removal from waters. In this study, Acid Red18 removal in batch mode by using Granular Ferric Hydroxide (GFH) was investigated. The GFH characterized by XRD, FESEM and FTIR analysis. Experiments were designed using RSM-CCD method. The maximum removal efficiency was obtained 78.59% at pH = 5, GFH dosage = 2 g/l, AR18 concentration = 77.5 mg/l and 85 min of contact time. Optimization with RSM and Genetic Algorithm carried out and is similar together. The non-linear adsorption Isotherm and kinetic fitted with Freundlich (R2 = 0.978) and pseudo-second-order (R2 = 0.989) models, respectively. Thermodynamic studies showed that the AR18 adsorption is endothermic process and GFH nature was found spontaneous.

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020).

Understanding the spatial distribution of coronavirus disease 2019 (COVID-19) cases can provide valuable information to anticipate the world outbreaks and in turn improve public health policies. In this study, the cumulative incidence rate (CIR) and cumulative mortality rate (CMR) of all countries affected by the new corona outbreak were calculated at the end of March and April, 2020. Prior to the implementation of hot spot analysis, the spatial autocorrelation results of CIR were obtained. Hot spot analysis and Anselin Local Moran's I indices were then applied to accurately locate high and low-risk clusters of COVID-19 globally. San Marino and Italy revealed the highest CMR by the end of March, though Belgium took the place of Italy as of 30th April. At the end of the research period (by 30th April), the CIR showed obvious spatial clustering. Accordingly, southern, northern and western Europe were detected in the high-high clusters demonstrating an increased risk of COVID-19 in these regions and also the surrounding areas. Countries of northern Africa exhibited a clustering of hot spots, with a confidence level above 95%, even though these areas assigned low CIR values. The hot spots accounted for nearly 70% of CIR. Furthermore, analysis of clusters and outliers demonstrated that these countries are situated in the low-high outlier pattern. Most of the surveyed countries that exhibited clustering of high values (hot spot) with a confidence level of 99% (by 31st March) and 95% (by 30th April) were dedicated higher CIR values. In conclusion, hot spot analysis coupled with Anselin local Moran's I provides a scrupulous and objective approach to determine the locations of statistically significant clusters of COVID-19 cases shedding light on the high-risk districts.

Characteristics and health risk assessment of polycyclic aromatic hydrocarbons associated with dust in household evaporative coolers.

This study reports a characterization of indoor polycyclic aromatic hydrocarbons (PAHs) associated with dust (dust-PAHs) in household evaporative coolers and their associated health effects. Extensive analysis showed that the indoor dust-PAHs stemmed mostly from pyrogenic sources (vehicular emissions) with mean total concentrations limited between 131 and 429 ng g-1. The distribution pattern of PAHs based on number of rings exhibited the following order of decreasing relative abundance: 4 > 3 > 5 > 6 > 2 rings. Results indicate that the mutagenicity of dust-PAHs exceeded their carcinogenicity, but that the potential carcinogenic effects are still significant. The mean lifetime cancer risk for different age groups for three pathways based on Model 2 (dermal (1.39 × 10-1 to 1.91 × 10-2), ingestion (2.13 × 10-3 to 8.08 × 10-3) and inhalation (1.62 × 10-7 to 4.06 × 10-7)) was 7.4-146 times higher than values predicted by Model 1 (dermal (5.13 × 10-5 to 3.03 × 10-3), ingestion (9.34 × 10-5 to 1.31 × 10-3) and inhalation (7.13 × 10-20 to 1.68 × 10-20)). Hence, exposure to dust-PAHs in household evaporative coolers lead to high risk, especially for children (less than 11 years) (HQ = 2.71 × 10-20 to 54.8 and LTCRs = 7.13 × 10-20 to 1.39 × 10-1). Strategies should be considered to eliminate such pollutants to protect people, especially children, from the non-carcinogenic and carcinogenic effects by changing household evaporative coolers with other cooling systems.