Short-, Medium-, and Long-Term Prediction of Carbon Dioxide Emissions using Wavelet-Enhanced Extreme Learning Machine

Carbon dioxide (CO2) is the main greenhouse gas responsible for global warming. Early prediction of CO2 is critical for developing strategies to mitigate the effects of climate change. A sophisticated version of the extreme learning machine (ELM), the wavelet enhanced extreme learning machine (W-EELM), is used to predict CO2 on different time scales (weekly, monthly, and yearly). Data were collected from the Mauna Loa Observatory station in Hawaii, which is ideal for global air sampling. Instead of the traditional method (singular value decomposition), a complete orthogonal decomposition (COD) was used to accurately calculate the weights of the ELM output layers. Another contribution of this study is the removal of noise from the input signal using the wavelet transform technique. The results of the W-EELM model are compared with the results of the classical ELM. Various statistical metrics are used to evaluate the models, and the comparative figures confirm the superiority of the applied models over the ELM model. The proposed W-EELM model proves to be a robust and applicable computer-based technology for modeling CO2concentrations, which contributes to the fundamental knowledge of the environmental engineering perspective. Doi: 10.28991/CEJ-2023-09-04-04 Full Text: PD

Novel deep eutectic solvent-functionalized carbon nanotubes adsorbent for mercury removal from water

Due to the interestingly tolerated physicochemical properties of deep eutectic solvents (DESs), they are currently in the process of becoming widely used in many fields of science. Herein, we present a novel Hg2+ adsorbent that is based on carbon nanotubes (CNTs) functionalized by DESs. A DES formed from tetra-n-butyl ammonium bromide (TBAB) and glycerol (Gly) was used as a functionalization agent for CNTs. This novel adsorbent was characterized using Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy, XRD, FESEM, EDX, BET surface area, and Zeta potential. Later, Hg2+ adsorption conditions were optimized using response surface methodology (RSM). A pseudo-second order model accurately described the adsorption of Hg2+. The Langmuir and Freundlich isotherms models described the absorption of Hg2+ on the novel adsorbent with acceptable accuracy. The maximum adsorption capacity was found to be 177.76 mg/g

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Probing the Effect of Gaseous Hydrocarbon Precursors on the Adsorptive Efficiency of Synthesized Carbon-based Nanomaterials

              The present work investigates the effect of the type of carbon precursor on the adsorptive proficiency of as-prepared carbon nanomaterials (CNMs) for the removal of methylene blue dye (MB) from aqueous media. A comparison study was applied to assess the growth of CNMs from the decomposition of methane (CNMY1) and acetylene (CNMY2) using response surface methodology with central composite design (RSM/CCD). The produced nanomaterials were characterized using FESEM, EDX, TEM, BET surface area, Raman, TGA, FTIR, and zeta potential. The as-prepared adsorbent displayed different morphologies and under the experimental conditions, 10 mg of CNMY1 and CNMY2 was responsible for 97.7 % and 96.80% removal of dye. The maximum adsorptive uptake predicted by Langmuir isotherm was about 250 and 174 mg/g for CNMY1 and CNMY2, respectively. The as-synthesized carbon nanomaterial in this study could be explored as a great potential candidate for dye-bearing wastewater treatment

Eutectic mixture-functionalized carbon nanomaterials for selective amperometric detection of nitrite using modified glassy carbon electrode

Choline chloride-urea (CU) (1:2 M ratio) was used to functionalize two types of carbon nanomaterials (CNMs); multiwall carbon nanotube (MWCNT) and graphene (Gr). A composite of both components was used to modify the surface of glassy carbon electrode (GCE) designated as (CU/MWCNT/Gr/GCE). CU-functionalized CNMs modified GCs have shown higher catalytic activity towards the oxidation of nitrite (NO2−) compared to bare and pristine CNM modified electrodes. However, the highest performance was found for a mixture of Gr and MWCNT (50% w/w). CU-functionalized CNMs were characterized by scanning electron microscope, Raman spectroscopy and X-ray diffraction. It has been hypothesized that covalent functionalization and exfoliation were responsible for the improvement in electrocatalytic activity of the modified electrode. CU/MWCNT/Gr/GCE presented a good stability, reproducibility with a detection limit 0.30 μM and a linear range between 5 μM–360 μM

Data-Driven Model for the Prediction of Total Dissolved Gas : Robust Artificial Intelligence Approach

Saturated total dissolved gas (TDG) is recently considered as a serious issue in the environmental engineering field since it standsbehind the reasons for increasing the mortality rates of fish and aquatic organisms. +e accurate and more reliable prediction ofTDG has a very significant role in preserving the diversity of aquatic organisms and reducing the phenomenon of fish deaths.Herein, two machine learning approaches called support vector regression (SVR) and extreme learning machine (ELM) have beenapplied to predict the saturated TDG% at USGS 14150000 and USGS 14181500 stations which are located in the USA. For theUSGS 14150000 station, the recorded samples from 13 October 2016 to 14 March 2019 (75%) were used for training set, and therest from 15 March 2019 to 13 October 2019 (25%) were used for testing requirements. Similarly, for USGS 14181500 station, thehourly data samples which covered the period from 9 June 2017 till 11 March 2019 were used for calibrating the models and from12 March 2019 until 9 October 2019 were used for testing the predictive models. Eight input combinations based on differentparameters have been established as well as nine statistical performance measures have been used for evaluating the accuracy ofadopted models, for instance, not limited, correlation of determination (R2), mean absolute relative error (MAE), and uncertaintyat 95% (U95). +e obtained results of the study for both stations revealed that the ELM managed efficiently to estimate the TDG incomparison to SVR technique. For USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported asR2 of 0.986 (0.986), MAE of 0.316 (0.441), and U95 of 3.592 (3.869). Lastly, for USGS 14181500 station, the statistical measures forELM (SVR) were, respectively, reported as R2 of 0.991 (0.991), MAE of 0.338 (0.396), and U95 of 0.832 (0.837). In addition, ELM’straining process computational time is stated to be much shorter than that of SVM. +e results also showed that the temperatureparameter was the most significant variable that influenced TDG relative to the other parameters. Overall, the proposed model(ELM) proved to be an appropriate and efficient computer-assisted technology for saturated TDG modeling that will contribute tothe basic knowledge of environmental considerations.Validerad;2021;Nivå 2;2021-04-19 (alebob);Finansiär: AlMaarif University College</p

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.Validerad;2022;Nivå 2;2022-10-03 (hanlid);Funder: Al-Maarif University College, Ramadi, Iraq</p

Growth and optimization of carbon nanotubes in powder activated carbon for an efficient removal of methylene blue from aqueous solution

<p>This work demonstrated the synthesis of carbon nanotubes (CNTs) on powder activated carbon (PAC) impregnated with Ni-catalyst through chemical vapour deposition. The optimized effects of reaction temperature, time and feedstock flow rates on CNT growth were examined. Potassium permanganate (KMnO₄) and potassium permanganate in acidic solution (KMnO₄/H₂SO₄) were used to functionalize CNTs samples. A primary screening of methylene blue (MB) adsorption was conducted. The chemical, physical and morphological properties of the adsorbent with the highest removal efficiency were investigated using FESEM, EDX, TEM, BET surface area, RAMAN, TGA, FTIR, and zeta potential. The resulting carbon nanotube-loaded activated carbons possessed abundant pore structure and large surface area. The MB removal by the as-synthesized CNTs was more remarkable than that by the modified samples. Adsorption studies were carried out to evaluate the optimum conditions, kinetics and isotherms for MB adsorption process. The response surface methodology-central composite design (RSM-CCD) was used to optimize the adsorption process parameters, including pH, adsorbent dosage and contact time. The investigation of the adsorption behaviour demonstrated that the adsorption was well fitted with the pseudo-second-order model and Langmuir isotherm with the maximum monolayer adsorption capacity of 174.5 mg/g. Meanwhile, the adsorption of MB onto adsorbent was driven by the electrostatic attraction and π-π interaction. Moreover, the as-obtained CNT-PAC exhibited good reusability after four repeated operations. In view of these empirical findings, the low-cost CNT-PAC has potential for removal of MB from aqueous solution.</p

Adsorption of 2,4-dichlorophenol from water using deep eutectic solvents-functionalized carbon nanotubes

In this work, novel adsorbents for 2,4-dichlorphenol (DCP) were introduced using deep eutectic solvent (DES) as functionalization agent for multi-wall carbon nanotubes (MWCNTs). Choline chloride salt (ChCl) was mixed with ethylene glycol (EG) as hydrogen bond donor (HBD) at molar ratio of (1:2) to prepare DES. Three DES-based MWCNTs adsorbents were produced and their chemical, physical and morphological properties were investigated using, RAMAN, FTIR, FESEM, zeta potential, TGA, TEM and BET surface area. The capability of DES as non-destructive functionalization agent for MWCNTs was proved by the increase of the purity and the surface area of MWCNTs. Response surface methodology was used to define the optimum conditions for 2,4-DCP adsorption onto each adsorbent. The adsorption experimental data were well described by pseudo-second order kinetic mode land by Langmuir isotherm model. DES-acid treated MWCNTs showed the highest maximum adsorption capacity of 390.53 mg g −1