Volume 25 - 2023

Developing an efficient sorbent for removing the highly toxic Cr(VI) is very important, which tackles the threat of Cr(VI) pollution to ecological environment and human health. Herein, a cationic covalent organic framework bearing cationic guanidine (named TATGCl) was synthesized from terephthalaldehyde (TA) and triaminoguanidinium chloride (TGCl), and exploited to discard Cr(VI) from water. The Cr(VI) removal ability of TATGCl strongly relies on the aqueous pH, and is strong at low pH. The removing ability is obviously also affected by ionic strength. The Cr(VI) sorption process on TATGCl obeys pseudo-second-order and Langmuir models. Batch adsorption experiments and characterization show that the guanidine group on the skeleton of TATGCl is pivotal in capturing Cr(VI) through hydrogen bonding and anion exchange/electrostatic interaction. A reduction mechanism also exists in the Cr(VI) disposal by a small amount of unreacted aldehyde group of TATGCl. The large removal ability makes TATGCl a prospective candidate for discarding Cr(VI) from water.

Tamarindus indica shell biochar is employed as an alternate adsorbent precursor for the removal of heavy metal ions from aqueous solutions. It investigated the Tamarindus indica shell biochar's capacity to absorb chromium (Cr), copper (Cu), and Lead (Pb). This study showed the extensive explored how biosorption experimental limitations counting primary metal attentiveness, adsorbent dosage, temperature, and contact time affect the process. The complete analysis of the Tamarindus indica shell's adsorption capability with respect to chromium, copper, and nickel removal was conducted using a batch adsorption procedure. Determining the amount of heavy metal removal in the aqueous solution proceeded by Gas Chromatography (GC). The experimental data analyzed using the Yoon Nelson and Thomson models to regulate the equilibrium isotherms. The optimal parameters for the overall adsorption model were determined by using ANOVA. Investigate the adsorbent's surface area to determine the presence of heavy metal presents using SEM, XRD, and FTIR techniques. Each researched heavy metal's adsorption capability is listed below: Cr = 6.07 mg/L, Cu = 5.53 mg/L and Pb = 5.497mg/L with a removal percentage of 64%, 92% and 78%, respectively. The results showed that biochar generated from Tamarindus indica shells is an effective adsorbent for removing copper from aqueous solutions but not a viable biosorbent for removing chromium. Also, the regenerated column's adsorption capability was examined. The outcomes of the research demonstrated that bio-char, which produced from Tamarindus indica shell can be employed as an efficient and reasonably priced adsorbent to remove heavy metal ions from aqueous solutions.

The amount of waste is increasing daily; however, the available land area is limited. This problem can be overcome by segregating waste and other alternatives by accelerating landfill stabilization using landfill bioreactors. In this study, anaerobic bioreactor A with daily use of water and the leachate, anaerobic bioreactor B with the addition of water and reuse of the leachate daily, and a control bioreactor with the addition of water were used. The parameters measured were the pH, temperature, humidity, chemical oxygen demand (COD), and biochemical oxygen demand (BOD) of the leachate. The reuse of the leachate and addition of water to the bioreactor accelerated waste degradation and caused high humidity. The average COD and BOD in each bioreactor on day 40 were stable. On day 45, the BOD (74.925 mg/L) was below 100 mg/L and the average COD (781 mg/L) was stable below 1000 mg/L. These results were for all bioreactors except for the control bioreactor, which only received water addition treatment; its COD value was below 1000 mg/L on day 50, at 989 mg/L. Waste decomposition was stabilized, with the BOD below 100 mg/L and the COD below 1000 mg/L.

In recent times, advanced technologies in transportation are developing like connected and automated vehicles and shared mobility services. A rapidly increasing number of vehicles in intelligent transportation system (ITS) and smart cities causes pollution and degrade the quality of air. Owing to the incredible effect of air quality on individual lives, it is indispensable to design a system by which air pollutants (PM2.5, NOx, COx, SOx) are predicted. But predicting air quality and its pollutants was complex since air quality relies on various elements like power plants, weather, and vehicular emissions. Deep learning (DL) and Machine learning (ML) techniques are leveraged for developing an air quality predictive method. This study develops an Air Quality Prediction utilizing Ensemble Voting based Deep Learning with Mud Ring Algorithm (AQP-EDLMRA) technique. The presented AQP-EDLMRA technique follows the ensemble voting model, which exploits three DL classification methods like long short-term memory (LSTM), deep belief network (DBN), and stacked autoencoder (SAE). Then, the new data can be classified by the weighted vote of their prediction outcomes. To adjust the hyperparameter values of the DL methods, the MRA was exploited, showing the novelty of the work. The simulation values of the AQP-EDLMRA approach are tested using a series of air quality data and the comprehensive comparative results demonstrated that the AQP-EDLMRA technique has reached improved forecasting performance.