Application of artificial intelligence in modeling of nitrate removal process using zero-valent iron nanoparticles-loaded carboxymethyl cellulose.

This study explores nitrate reduction in aqueous solutions using carboxymethyl cellulose loaded with zero-valent iron nanoparticles (Fe0-CMC). The structures of this nano-composite were characterized using various techniques. Based on the characterization results, the specific surface area of Fe0-CMC measured by the Brunauer-Emmett-Teller analysis were 39.6 m2/g. In addition, Scanning Electron Microscopy images displayed that spherical nano zero-valent iron particles (nZVI) with an average particle diameter of 80 nm are surrounded by carboxymethyl cellulose and no noticeable aggregates were detected. Batch experiments assessed Fe0-CMC's effectiveness in nitrate removal under diverse conditions including different adsorbent dosages (Cs, 2-10 mg/L), contact time (t, 10-1440 min), initial pH (pHi, 2-10), temperature (T, 10-55 °C), and initial concentration of nitrate (C0, 10-500 mg/L). Results indicated decreased removal with higher initial pHi and C0, while increased Cs and T enhanced removal. The study of nitrate removal mechanism by Fe0-CMC revealed that the redox reaction between immobilized nZVI on the CMC surface and nitrate ions was responsible for nitrate removal, and the main product of this reaction was ammonium, which was subsequently completely removed by the synthesized nanocomposite. In addition, a stable deviation quantum particle swarm optimization algorithm (SD-QPSO) and a least square error method were employed to train the ANFIS parameters. To demonstrate model performance, a quadratic polynomial function was proposed to display the performance of the SD-QPSO algorithm in which the constant parameters were optimized through the SD-QPSO algorithm. Sensitivity analysis was conducted on the proposed quadratic polynomial function by adding a constant deviation and removing each input using two different strategies. According to the sensitivity analysis, the predicted removal efficiency was most sensitive to changes in pHi, followed by Cs, T, C0, and t. The obtained results underscore the potential of the ANFIS model (R2 = 0.99803, RMSE = 0.9888), and polynomial function (R2 = 0.998256, RMSE = 1.7532) as accurate and efficient alternatives to time-consuming laboratory measurements for assessing nitrate removal efficiency. These models can offer rapid insights and predictions regarding the impact of various factors on the process, saving both time and resources.

Advances in microbial exoenzymes bioengineering for improvement of bioplastics degradation.

Plastic pollution has become a major global concern, posing numerous challenges for the environment and wildlife. Most conventional ways of plastics degradation are inefficient and cause great damage to ecosystems. The development of biodegradable plastics offers a promising solution for waste management. These plastics are designed to break down under various conditions, opening up new possibilities to mitigate the negative impact of traditional plastics. Microbes, including bacteria and fungi, play a crucial role in the degradation of bioplastics by producing and secreting extracellular enzymes, such as cutinase, lipases, and proteases. However, these microbial enzymes are sensitive to extreme environmental conditions, such as temperature and acidity, affecting their functions and stability. To address these challenges, scientists have employed protein engineering and immobilization techniques to enhance enzyme stability and predict protein structures. Strategies such as improving enzyme and substrate interaction, increasing enzyme thermostability, reinforcing the bonding between the active site of the enzyme and substrate, and refining enzyme activity are being utilized to boost enzyme immobilization and functionality. Recently, bioengineering through gene cloning and expression in potential microorganisms, has revolutionized the biodegradation of bioplastics. This review aimed to discuss the most recent protein engineering strategies for modifying bioplastic-degrading enzymes in terms of stability and functionality, including enzyme thermostability enhancement, reinforcing the substrate binding to the enzyme active site, refining with other enzymes, and improvement of enzyme surface and substrate action. Additionally, discovered bioplastic-degrading exoenzymes by metagenomics techniques were emphasized.

Simulating fate and transport of non-steroidal anti-inflammatory drugs using Root Zone Water Quality Model 2.

The fate and transport of non-steroidal anti-inflammatory drugs (NSAIDs) in soil are determined by various processes, and the complexity of the system lends itself to the use of computer simulation models to help understand it. This study demonstrated the first attempt to use empirical data from lab incubation and field studies to parameterize and test a process-based agricultural systems model, Root Zone Water Quality Model 2 (RZWQM2), to simulate the fate and transport of naproxen (NPX), ibuprofen (IBF), and ketoprofen (KTF) in field-based lysimeters amended with alkaline-treated biosolids (ATBs). The model calibrated for the soil-water balance module and contaminant transport module was used to predict water seepage through the soil profile in 2017 and 2018 within a 15% error of the field measured data, with model performance statistics such as Nash-Sutcliffe model efficiency (NSE) and R2 all greater than 0.70. The overall predicted percent recovery of initial spiked NSAIDs in both soil and water samples, after further calibration of the contaminant transport module, was within the same order of magnitude as the measured data. The model underestimated the percent recovery of initial spiked NSAIDs at the 30- to 55-cm soil depth for all treatments on day 3. The calibrated soil subsurface aerobic half-lives of NPX and IBF were found to be considerably lower than their laboratory-measured half-lives obtained from the incubation study. The overall performance of RZWQM2 in simulating the soil hydrology and behavior of NSAIDs in soil profiles receiving various rates of ATB amendments was satisfactory.