Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad.

This study seeks to investigate the impact of COVID-19 lockdown measures on air quality in the city of Mashhad employing two strategies. We initiated our research using basic statistical methods such as paired sample t-tests to compare hourly PM2.5 data in two scenarios: before and during quarantine, and pre- and post-lockdown. This initial analysis provided a broad understanding of potential changes in air quality. Notably, a low reduction of 2.40% in PM2.5 was recorded when compared to air quality prior to the lockdown period. This finding highlights the wide range of factors that impact the levels of particulate matter in urban settings, with the transportation sector often being widely recognized as one of the principal causes of this issue. Nevertheless, throughout the period after the quarantine, a remarkable decrease in air quality was observed characterized by distinct seasonal patterns, in contrast to previous years. This finding demonstrates a significant correlation between changes in human mobility patterns and their influence on the air quality of urban areas. It also emphasizes the need to use air pollution modeling as a fundamental tool to evaluate and understand these linkages to support long-term plans for reducing air pollution. To obtain a more quantitative understanding, we then employed cutting-edge machine learning methods, such as random forest and long short-term memory algorithms, to accurately determine the effect of the lockdown on PM2.5 levels. Our models' results demonstrated remarkable efficacy in assessing the pollutant concentration in Mashhad during lockdown measures. The test set yielded an R-squared value of 0.82 for the long short-term memory network model, whereas the random forest model showed a calculated cross-validation R-squared of 0.78. The required computational cost for training the LSTM and the RF models across all data was 25 min and 3 s, respectively. In summary, through the integration of statistical methods and machine learning, this research attempts to provide a comprehensive understanding of the impact of human interventions on air quality dynamics.

Investigation of adsorption and biosorption features of bio-functionalized poly(GMA-Co-EGDMA) polymer beads in the treatment of nicotine from tobacco industry.

The investigation of multifunctional materials for modern enzyme immobilization is an attractive subject in advanced adsorption and biosorption applications. In the present study, the feasibility of immobilization of Lipozyme TL 100L (LPZM) on 3-aminopropyltriethoxysilane (APTES) modified poly-(GMA-co-EGDMA) (PEGDMA) was investigated for adsorption and biosorption of nicotine from aqueous solution. Characterization tests confirmed successful immobilization of lipozyme which significantly altered thermal behavior, surface characteristics, and surface morphology of PEGDMA and PEGDMA/APTES. In addition, the immobilization yields were calculated as 85.0% and 72.0% onto PEGDMA/APTES using physical adsorption and covalent immobilization methods, respectively. The nicotine removal efficiencies were calculated to be 66.4%, 79.0%, 98.9%, and 85.7%, using raw PEGDMA, PEGDMA/APTES, PEGDMA/APTES@LPZM, and PEGDMA/APTES/GU@LPZM, respectively. For the raw PEGDMA, the Langmuir isotherm was best fitted to the adsorption data, while Langmuir-Freundich model described well the adsorption process on PEGDMA/APTES and PEGDMA/APTES@LPZM. The maximum adsorption capacities of Langmuir-Freundlich model increased from 8.118 to 17.32 mg/g after enzyme immobilization. The negative enthalpy value, ΔH° (- 10.37 kJ/mol), revealed that the nicotine adsorption on PEGDMA/APTES@LPZM was exothermic in nature, which was corroborated by the decrease observed in the number of adsorbed molecules with increasing temperature. In the kinetic experiments, the adsorption on PEGDMA and PEGDMA/APTES@LPZM reached equilibrium with the removal percentages as 66.4% and 98.9% at the end of 3 h, respectively. The nicotine adsorption performances in real water matrices were also investigated, and PEGDMA/APTES@LPZM showed satisfactory reusability with removal percentage decreased from 98.9% (1st cycle) to 83.0% (6th cycle).

Removal of naproxen from wastewater using chitosan-aerogel-activated carbon biocomposites: Theory, equilibrium, kinetics, thermodynamics, and process optimization.

This study investigated the performance of chitosan-aerogel-activated carbon (CHT:AEO:AC) biocomposite as an adsorbent for the removal of naproxen from wastewater. Naproxen removal in % was 99, 33, 62, and 90 using 300 mg of raw AC, raw CHT, CHT:AEO, and CHT:AEO:AC, respectively. Langmuir, Freundlich, Dubinin-Radushkevich (D-R), and Temkin isotherm models were used to obtain adsorption isotherms. Chi-squared (χ2 ) and correlation coefficients (R2 ) values showed that the parameters of the Freundlich, Temkin, and D-R models were more suitable for naproxen adsorption than the Langmuir model for raw CHT, CHT:AEO, and CHT:AEO:AC, whereas the Langmuir model fitted well for raw AC. The adsorption of naproxen onto biocomposites was defined by pseudo-second-order kinetic model, and adsorption rate constants were 0.245, 0.036, 0.075, and 0.147 mg g-1  min-1 for raw AC, raw CHT, CHT:AEO, and CHT:AEO:AC, respectively. The impact of optimum process conditions on naproxen adsorption was explored using response surface methodology. The optimum independent variables were 288.94 mg, 29.64°C, and 372.5 min, leading to a rate of naproxen removal onto CHT:AEO:AC of 90.29%. PRACTITIONER POINTS: Naproxen adsorption from wastewater using chitosan-aerogel-activated carbon biocomposite (CHT:AEO:AC) was investigated. The effects of the amount of biocomposite, temperature, and time on the adsorption were investigated. Optimization of the process conditions was carried out using the response surface methodology.

Activated carbon embedded alginate beads for removing nonsteroidal anti-inflammatory drug naproxen from wastewater: equilibrium, kinetics, thermodynamics, desorption, and reusability.

In the present study, activated carbon/alginate (AC/ALG) beads were successfully synthesized with different AC:ALG ratios of 1.0-3.0 (w/v) and used for the adsorption of the nonsteroidal anti-inflammatory drug naproxen from wastewater. The beads were characterized by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET) analysis, and adsorbent dosage, initial pH, initial naproxen concentration, and contact time in removal efficiency were investigated. Maximum naproxen removal percentage was achieved using 350 mg of AC/ALG beads with a ratio of 3.0% (w/v) within six hours and naproxen removal performance was determined to be 98.0%. Freundlich, Temkin, and Dubinin-Radushkevich (D-R) isotherm models were fitted to the equilibrium data better than the Langmuir model. According to kinetics results, the equilibrium time for the AC/ALG beads was reached in four hours and the kinetic model was determined by the pseudo-second-order equation. The thermodynamic parameters were calculated and enthalpy of naproxen adsorption was found to be positive for all AC/ALG beads. After the adsorption process the beads can easily be regenerated by ethanol and reused within seven cycles.