Forest fires are sudden, destructive, hazardous, and challenging to manage and rescue, earning them a place on UNESCO's list of the world's eight major natural disasters. Currently, amid global warming, all countries worldwide have entered a period of high forest fire incidence. Due to global warming, the frequency of forest fires has accelerated, the likelihood of large fires has increased, and the spatial and temporal dynamics of forest fires have shown different trends. Therefore, the impact of climate change on the spatiotemporal dynamics of forest fires has become a hot issue in the field of forest fire research in recent years. Therefore, it is of great significance and necessity to conduct a review of the research in this area. This review delves into the interactions and impacts between climate change and the spatiotemporal dynamics of forest fires. To address this issue, scholars have mainly adopted the following research methods: first, statistical analysis methods, second, the establishment of spatiotemporal prediction models for meteorology and forest fires, and third, the coupling of climate models with forest fire risk forecasting models. The statistical analysis method relies on the analysis of historical meteorological and fire-related data to study the effects of climate change and meteorological factors on fire occurrence. Meanwhile, forest fire prediction models utilize technical tools such as remote sensing. These models synthesize historical meteorological and fire-related data, incorporating key meteorological factors such as temperature, rainfall, relative humidity, and wind. The models revealed the spatial and temporal distribution patterns of fires, identified key drivers, and explored the interactions between climate change and forest fire dynamics, culminating in the construction of predictive models. With the deepening of the study, the coupling of climate models and fire risk ranking systems became a trend in the prediction of forest fire risk trends. Moreover, as the climate warms, the increased frequency of extreme weather events like heatwaves, droughts, snow and ice storms, and El Niño-Southern Oscillation (ENSO) has accelerated forest fire occurrences and raised the risk of major fires. This review offers valuable technical insights by comprehensively analyzing the spatial and temporal characteristics of forest fires, elucidating key meteorological drivers, and exploring potential mechanisms. These insights serve as a scientific foundation for preventive measures and effective forest fire management. In the face of a changing climate, this synthesis contributes to the development of informed strategies to mitigate the escalating threat of forest fires.
The adsorption of ammonium from water was studied on an activated carbon obtained using raw oil palm shell and activated with acetic acid. The performance of this adsorbent was tested at different operating conditions including the solution pH, adsorbent dosage, and initial ammonium concentration. Kinetic and equilibrium studies were carried out, and their results were analyzed with different models. For the adsorption kinetics, the pseudo-first order equation was the best model to correlate this system. Calculated adsorption rate constants ranged from 0.071 to 0.074 g/mg min. The ammonium removal was 70-80% at pH 6-8, and it was significantly affected by electrostatic interaction forces. Ammonium removal (%) increased with the adsorbent dosage, and neutral pH condition favored the adsorption of this pollutant. The best ammonium adsorption conditions were identified with a response surface methodology model where the maximum removal was 91.49% with 2.27 g/L of adsorbent at pH 8.11 for an initial ammonium concentration of 36.90 mg/L. The application of a physical monolayer model developed by statistical physics theory indicated that the removal mechanism of ammonium was multi-ionic and involved physical interactions with adsorption energy of 29 kJ/mol. This activated carbon treated with acetic acid is promising to depollute aqueous solutions containing ammonium.
The research aimed to determine the influence of endophytic fungi on tolerance, growth and phytoremediation ability of Prosopis juliflora in heavy metal-polluted landfill soil. A consortium of 13 fungal isolates as well as Prosopis juliflora Sw. DC was used to decontaminate heavy metal-polluted landfill soil. Enhanced plant growth (biomass and root and shoot lengths) and production of carotenoids, chlorophyll and amino acids L-phenylalanine and L-leucine that are known to enhance growth were found in the treated P. juliflora. Better accumulations of heavy metals were observed in fungi-treated P. juliflora over the untreated one. An upregulated activity of peroxidase, catalase and ascorbate peroxidase was recorded in fungi-treated P. juliflora. Additionally, other metabolites, such as glutathione, 3,5,7,2',5'-pentahydroxyflavone, 5,2'-dihydroxyflavone and 5,7,2',3'-tetrahydroxyflavone, and small peptides, which include Lys Gln Ile, Ser Arg Ala, Asp Arg Gly, Arg Ser Ser, His His Arg, Arg Thr Glu, Thr Arg Asp and Ser Pro Arg, were also detected. These provide defence supports to P. juliflora against toxic metals. Inoculating the plant with the fungi improved its growth, metal accumulation as well as tolerance against heavy metal toxicity. Such a combination can be used as an effective strategy for the bioremediation of metal-polluted soil.
The objective of this research is to create a highly effective approach for eliminating pollutants from the environment through the process of photocatalytic degradation. The study centers around the production of composites consisting of CaCu3Ti4O12 (CCTO) and reduced graphene oxide (rGO) using an ultrasonic-assisted method, with a focus on their capacity to degrade ibuprofen (IBF) and ciprofloxacin (CIP) via photodegradation. The impact of rGO on the structure, morphology, and optical properties of CCTO was inspected using XRD, FTIR, Raman, FESEM, XPS, BET, and UV-Vis. Morphology characterization showed that rGO particles were dispersed within the CCTO matrix without any specific chemical interaction between CCTO and C in the rGO. The BET analysis revealed that with increasing the amount of rGO in the composite, the specific surface area significantly increased compared to the CCTO standalone. Besides, increasing rGO resulted in a reduction in the optical bandgap energy to around 2.09 eV, makes it highly promising photocatalyst for environmental applications. The photodegradation of IBF and CIP was monitored using visible light irradiation. The results revealed that both components were degraded above 97% after 60 min. The photocatalyst showed an excellent reusability performance with a slight decrease after five runs to 93% photodegradation efficiency.
Guided by efficient utilization of natural plant oil and sulfur as low-cost sorbents, it is desired to tailor the porosity and composition of polysulfides to achieve their optimal applications in the management of aquatic heavy metal pollution. In this study, polysulfides derived from soybean oil and sulfur (PSSs) with improved porosity (10.2-22.9 m2/g) and surface oxygen content (3.1-7.0 wt.%) were prepared with respect to reaction time of 60 min, reaction temperature of 170 °C, and mass ratios of sulfur/soybean oil/NaCl/sodium citrate of 1:1:3:2. The sorption behaviors of PSSs under various hydrochemical conditions such as contact time, pH, ionic strength, coexisting cations and anions, temperature were systematically investigated. PSSs presented a fast sorption kinetic (5.0 h) and obviously improved maximum sorption capacities for Pb(II) (180.5 mg/g), Cu(II) (49.4 mg/g), and Cr(III) (37.0 mg/g) at pH 5.0 and T 298 K, in comparison with polymers made without NaCl/sodium citrate. This study provided a valuable reference for the facile preparation of functional polysulfides as well as a meaningful option for the removal of aquatic heavy metals.
The ubiquitous proximity of the commonly used microplastic (MP) particles particularly polyethylene (PE), polypropylene (PP), and polystyrene (PS) poses a serious threat to the environment and human health globally. Biological treatment as an environment-friendly approach to counter MP pollution has recent interest when the bio-agent has beneficial functions in their ecosystem. This study aimed to utilize beneficial floc-forming bacteria Bacillus cereus SHBF2 isolated from an aquaculture farm in reducing the MP particles (PE, PP, and PS) from their environment. The bacteria were inoculated for 60 days in a medium containing MP particle as a sole carbon source. On different days of incubation (DOI), the bacterial growth analysis was monitored and the MP particles were harvested to examine their weight loss, surface changes, and alterations in chemical properties. After 60 DOI, the highest weight loss was recorded for PE, 6.87 ± 0.92%, which was further evaluated to daily reduction rate (k), 0.00118 day-1, and half-life (t1/2), 605.08 ± 138.52 days. The OD value (1.74 ± 0.008 Abs.) indicated the higher efficiency of bacteria for PP utilization, and so for the colony formation per define volume (1.04 × 1011 CFU/mL). Biofilm formation, erosions, cracks, and fragments were evident during the observation of the tested MPs using the scanning electron microscope (SEM). The formation of carbonyl and alcohol group due to the oxidation and hydrolysis by SHBF2 strain were confirmed using the Fourier transform infrared spectroscopic (FTIR) analysis. Additionally, the alterations of pH and CO2 evolution from each of the MP type ensures the bacterial activity and mineralization of the MP particles. The findings of this study have confirmed and indicated a higher degree of biodegradation for all of the selected MP particles. B. cereus SHBF2, the floc-forming bacteria used in aquaculture, has demonstrated a great potential for use as an efficient MP-degrading bacterium in the biofloc farming system in the near future to guarantee a sustainable green aquaculture production.
Although hybrid wind-biomass-battery-solar energy systems have enormous potential to power future cities sustainably, there are still difficulties involved in their optimal planning and designing that prevent their widespread adoption. This article aims to develop an optimal sizing of microgrids by incorporating renewable energy (RE) technologies for improving cost efficiency and sustainability in urban areas. Diverse RE technologies such as photovoltaic (PV) systems, biomass, batteries, wind turbines, and converters are considered for system configuration to obtain this goal. Net present cost (NPC) is this study's objective function for optimal sizing microgrid configuration. For demonstration, we assess the technical, economic factors, and atmospheric emissions of optimal hybrid renewable energy systems for Putrajaya City in Malaysia. The required solar radiation data, temperature, and wind speeds are collected from the NASA surface metrological database. From the quantitative analysis of simulations, the biomass-battery-based system has optimal economic outcomes compared to other systems with an NPC of around 1.07 M$, while the cost of energy (COE) is 0.118 $/kWh. Moreover, environmentally safe nitrogen oxide emissions, carbon monoxide, and carbon dioxide concentrations exist. The grid-tied RE technology boasts cost-effectiveness, with an NPC of 348,318 $ and a COE of 0.0112 $/kWh. This study aids decision-makers in formulating policies for integrating hybrid RE systems in urban areas, promoting sustainable energy generation.
The heavy metals namely Fe, As, Cu, Cd, and Pb were investigated in two marine fishes silver pomfret (Pampus argentus) and torpedo scad (Megalaspis cordyla), and three seafoods sibogae squid (Loligo sibogae), Indian white prawn (Fenneropenaeus indicus), and mud crab (Scylla serrata) by using inductively coupled plasma spectrophotometer (ICP-MS) from two renowned fish harvesting coastal area of Malaysia named as Kedah and Selangor. Among the target heavy metals, highest mean concentration of As and Fe were found in Scylla serrata (72.14±7.77 μg/g) in Kedah and Megalaspis cordyla (149.40±2.15 μg/g) in Selangor. Pearson's correlation results showed As-Fe-Cd-Cu originated from the same source. Maximum estimated daily intake (EDI) values of Scylla serrata were found 175.25 μg/g/day and 100.81 μg/g/day for child in both Kedah and Selangor areas respectively. Hazard quotient (HQ) and hazard index (HI) results revealed that local consumers of Kedah and Selangor will face high chronic risk if they consume Scylla serrata, Fenneropenaeus indicus, and Megalaspis cordyla on regular basis in their diet. Carcinogenic risk results suggested that all the studied species pose very high risk of cancer occurrences to the consumers in both areas. Therefore, it could be recommended that consumers should be aware when they are consuming these marine species since they can pose serious health risk associated with prolonged consumption.
This study investigates how temperature and forward osmosis (FO) membrane properties, such as water permeability (A), solute permeability (B), and structural parameter (S), affect the specific energy consumption (SEC) of forward osmosis-reverse osmosis system. The results show that further SEC reduction beyond the water permeability of 3 LMH bar-1 is limited owing to high concentration polarization (CP). Increasing S by 10-fold increases FO recovery by 177.6%, causing SEC decreases by 33.6%. However, membrane with smaller S also increases external CP. To reduce SEC, future work should emphasize mixing strategies to reduce external CP. Furthermore, increasing the temperature from 10 to 40 °C can reduce SEC by 14.3%, highlighting the energy-saving potential of temperature-elevated systems. The factorial design indicates that at a lower temperature, increasing A and decreasing S have a more significant impact on reducing SEC. This underlines the importance of developing advanced FO membranes, particularly for lower-temperature processes.
River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.
Dye-sensitized solar cell (DSSC) is a photovoltaic device that can be produced from natural source pigments or natural dyes. The selection of natural dyes for DSSC application is currently under research. The utilization of natural dye materials that are easy to obtain, cost-effective, and non-toxic can reduce waste during DSSC fabrication. Natural dyes can be extracted from plants through extraction and chromatography methods. The suitability and viability of utilizing natural dyes as photosensitizers in DSSCs can be predicted using appropriate software simulation by varying related parameters to produce high power conversion efficiency. In this context, the purpose of the review is to highlight the evolution of performance improvement in the development of DSSCs with consideration of natural dye extraction and software simulation. This review also focuses on the results of extracting natural dyes from herbal ingredients, which are still very limited in information, and several parts of herbal plants that can be used as natural dye sources in the future of solid-state DSSCs have been identified. Based on the results of this review, the highest efficiency was obtained for the DSSC that used chlorophyll pigments as natural dyes using Peltophorum pterocarpum leaves with 6.07%, followed by anthocyanin pigments as natural dyes using raspberries (black) fruits with 1.5%, flavonoid pigments as natural dyes using Curcuma longa herbs with 0.64%, and flavonoid pigments as natural dyes using Indigofera tinctoria flowers with 0.46%.
This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and NO x emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide ( CO 2 ) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance.
The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model's output. The results of SHAP showed that river discharge has the strongest impact on the model's output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model's output (DL model is as black-box model) are recommended in future research.
This work has focused on the co-pyrolysis of sugarcane waste (SW) with polyethylene terephthalate (PET) to gain insight on its thermal decomposition, product distribution, kinetics, and synergistic effect. SW and PET were blended at different ratios (100:0, 80:20, 60:40, 40:60, and 0:100), and the Coats-Redfern method was used to determine the kinetics parameters. To ascertain the synergistic effect between SW and PET, product yields and composition of chemicals were compared with the synergistic effect of the individual components of pyrolysis. The bio-oil yield was significant at 60% of PET, with a difference of 19.41 wt% compared to the theoretical value. The synergistic impact of SW:PET on ester formation and acid compound inhibition was the most dominant at the 60:40 ratio. The kinetics analysis revealed that the diffusion mechanism, power law, and order of reactions were the most probable reaction models that can explain the pyrolysis of SW, and PET, and their blends. The resultant co-pyrolysis oil contained slightly larger hydrogen and carbon contents with low oxygen, and sulphur, and nitrogen contents, which improved the quality of the bio-oil. The results of this work could be used as a guide in selecting proper reaction conditions with optimal synergy during the co-pyrolysis process.
This study investigated the potential use of microalgae as partial cement replacement to heal cracks in cement mortar. Microbially induced calcite (CaCO3) precipitation (MICP) from Arthrospira platensis (A. platensis) (UMACC162) was utilised for crack-healing applications. Microalgae was cultivated in Kosaric Media (KM) together with filtered cement water (FCW), and used as a cement replacement material. The microalgal species was further evaluated for its capacity and adaptability towards large-scale culturing. The results showed that A. platensis could adapt and survive in cement water solution and cement mortar, suggesting the potential for self-healing in cement mortar. Further, the cultured species grown in both conditions (KM and KM & FCW) were harvested and incorporated into the cement mortar as a partial cement replacement material at different levels of 5%, 10%, 20%, and 30% of cement weight. The cement mortars partially replaced with microalgae were cured in water for 28 days. Pre-cracks were induced in the cured mortar with the 75% of their ultimate load. It took just 14 days for the microalgae-incorporated mortar to heal the cracks. The specimens with microalgae cultured in FCW showed a better performance and recovered 59% of their strength, with a maximum healed crack width of 0.7 mm. In terms of water tightness and porosity, they are comparable to the control mortar. The compressive strength measurements indicated the formation of calcite aggregate (crystal) that sealed the surface cracks, which was confirmed by a microstructural analysis. The results also demonstrate that the incorporation of microalgae into cement produced a self-healing effect, providing a new direction for crack healing. Additionally, the investigation indicated that replacing cement with microalgae reduced CO2 emissions by as much as 30%, with a substitution of 30% of microalgae. Exploring microalgae as a cement replacement could reduce carbon emissions and improve the state of the environment.
Mangroves provide essential ecosystem services including coastal protection by acting as coastal greenbelts; however, human-driven anthropogenic activities altered their existence and ecosystem functions worldwide. In this study, the successive degradation of the second largest mangrove forest, Chakaria Sundarbans situated at the northern Bay of Bengal part of Bangladesh was assessed using remote sensing approaches. A total of five multi-temporal Landsat satellite imageries were collected and used to observe the land use land cover (LULC) changes over the time periods for the years 1972, 1990, 2000, 2010, and 2020. Further, the supervised classification technique with the help of support vector machine (SVM) algorithm in ArcGIS 10.8 was used to process images. Our results revealed a drastic change of Chakaria Sundarbans mangrove forest, that the images of 1972 were comprised of mudflat, waterbody, and mangroves, while the images of 1990, 2000, 2010, and 2020 were classified as waterbody, mangrove, saltpan, and shrimp farm. Most importantly, mangrove forest was the largest covering area a total of 64.2% in 1972, but gradually decreased to 12.7%, 6.4%, 1.9%, and 4.6% for the years 1990, 2000, 2010, and 2020, respectively. Interestingly, the rate of mangrove forest area degradation was similar to the net increase of saltpan and shrimp farms. The kappa coefficients of classified images were 0.83, 0.87, 0.80, 0.87, and 0.91 with the overall accuracy of 88.9%, 90%, 85%, 90%, and 93.3% for the years 1972, 1990, 2000, 2010, and 2020, respectively. By analyzing normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and transformed difference vegetation index (TDVI), our results validated that green vegetated area was decreased alarmingly with time in this study area. This destruction was mainly related to active human-driven anthropogenic activities, particularly creating embankments for fish farms or salt productions, and cutting for collection of wood as well. Together all, our results provide clear evidence of active anthropogenic stress on coastal ecosystem health by altering mangrove forest to saltpan and shrimp farm saying goodbye to the second largest mangrove forest in one of the coastal areas of the Bay of Bengal, Bangladesh.
Every structure might be exposed to fire at some point in its lifecycle. The ability of geopolymer composites to withstand the effects of fire damage early before it is put out is of great importance. This study examined the effects of fire on geopolymer composite samples made with high-calcium fly ash and alkaline solution synthesised from waste banana peduncle and silica fume. A ratio of 0.30, 0.35, and 0.4 was used in the study for the alkaline solution to fly ash. Also used were ratios of 0.5, 0.75, and 1 for silica oxide (silica fume) to potassium hydroxide ratio. The strength loss, residual compressive strength, percentage strength loss, relative residual compressive strength, ultrasonic pulse velocity, and microstructural properties of the thirteen mortar mixes were measured after exposure to temperatures of 200, 400, 600, and 800 °C for 1 h, respectively. The results reveal that geopolymer samples exposed to elevated temperatures showed great dimensional stability with no visible surface cracks. There was a colour transition from dark grey to whitish brown for the green geopolymer mortar and brown to whitish brown for the control sample. As the temperature rose, weight loss became more pronounced, with 800 °C producing the most significant weight reduction. The optimum mixes had a residual compressive strength of 25.02 MPa after being exposed to 200 °C, 18.72 MPa after being exposed to 400 °C, 14.04 MPa after being exposed to 600 °C, and 7.41 MPa after being exposed to 800 °C. The control had a residual compressive strength of 8.45 MPa after being exposed to 200 °C, 6.67 MPa after being exposed to 400 °C, 3.16 MPa after being exposed to 600 °C, and 2.23 MPa after being exposed to 800 °C. The relative residual compressive strength decreases for green geopolymer mortar are most significant at 600 and 800 °C, with an average decrease of 0.47 and 0.30, respectively. The microstructure of the samples revealed various phase changes and new product formations as the temperature increased.
Alkali sludge (AS) is waste abundantly generated from solar photovoltaic (PV) solar cell industries. Since this potential basic material is still underutilized, a combination with NiO catalyst might greatly influence coke resentence, especially in high-temperature thermochemical reactions (Arora and Prasad, RSC Adv. 6:108,668-108688, 2016). This paper investigated alkaline sludge containing 3CaO-2SiO2 doped with well-known NiO to enhance the dry reforming of methane (DRM) reaction. The wet-impregnation method was used to prepare the xNiO/AS (x = 5-15%) catalysts. Subsequently, all catalysts were tested by using X-ray diffraction (XRD), nitrogen adsorption/desorption (BET), temperature-programmed reduction of hydrogen (H2-TPR), temperature-programmed desorption of carbon dioxide (TPD-CO2), field emission scanning electron microscopy (FESEM-EDX), and X-ray photoelectron spectroscopy (XPS). The spent catalysts were analyzed by thermogravimetric analysis (TGA/DTG), transmission electron microscopy (TEM), and temperature-programmed oxidation (TPO). The catalytic performance of xNiO/AS catalysts was investigated in a fixed bed reactor connected with gas chromatography thermal conductivity detector (GC-TCD) at a CH4:CO2 flow rate of 30 mL-1 during a 10-h reaction by following (Shamsuddin et al., Int. J. Energy Res. 45:15,463-15,480, 2021d). For optimization parameters, the effects of NiO concentration (5, 10, and 15%), reaction temperature (700, 750, 800, 850, and 900 °C), catalyst loading (0.1, 0.2, 0.3, 0.4, and 0.5 g), and gas hourly space velocity (GHSV) range from 3000, 6000, 9000, 12,000, and 15,000 h-1 were evaluated. The results showed that physical characteristics such as BET surface area and porosity do not significantly impact NiO percentages of dispersion, whereas chemical characteristics like reducibility are crucial for the catalysts' efficient catalytic activity. Due to the active sites on the catalyst surface being more accessible, increased NiO dispersion resulted in higher reactant conversion. The catalytic performance on various parameters that showed 15%NiO/AS exhibited high reactant conversion up to 98% and 40-60% product selectivity in 700 °C, 0.2 g catalyst loading, and 12,000 h-1 GHSV. According to spent catalyst analyses, the catalyst was stable even after the DRM reaction. Meanwhile, increased reducibility resulted in more and better active site formation on the catalyst. Synergetic effect of efficient NiO as active metal and medium basic sites from AS enhanced DRM catalytic activity and stability with low coke formation.
The advanced oxidation process (AOP) is an efficient method to treat recalcitrance pollutants such as pharmaceutical compounds. The essential physicochemical factors in AOP experiments significantly influence the efficiency, speed, cost, and safety of byproducts of the treatment process. In this review, we collected recent articles that investigated the elimination of pharmaceutical compounds by various AOP systems in a water medium, and then we provide an overview of AOP systems, the formation mechanisms of active radicals or reactive oxygen species (ROS), and their detection methods. Then, we discussed the role of the main physicochemical parameters (pH, chemical interference, temperature, catalyst, pollutant concentration, and oxidant concentration) in a critical way. We gained insight into the most frequent scenarios for the proper and improper physicochemical parameters for the degradation of pharmaceutical compounds. Also, we mentioned the main factors that restrict the application of AOP systems in a commercial way. We demonstrated that a proper adjustment of AOP experimental parameters resulted in promoting the treatment performance, decreasing the treatment cost and the treatment operation time, increasing the safeness of the system products, and improving the reaction stoichiometric efficiency. The outcomes of this review will be beneficial for future AOP applicants to improve the pharmaceutical compound treatment by providing a deeper understanding of the role of the parameters. In addition, the proper application of physicochemical parameters in AOP systems acts to track the sustainable development goals (SDGs).
This research provides a comprehensive analysis of groundwater pollution in the Lower Anayari Catchment (LAC) through δ2H and δ18O isotopic analysis, along with positive matrix factorization (PMF) and PCS-MLR receptor models. Forty groundwater samples were collected from hand-dug wells and equipped boreholes across the LAC. Flame photometry for Na+ and K+, complexometric titration for Ca2+, ion chromatography for Cl-, F-, NO3-, SO42-, and PO43-, and atomic absorption spectrometry for Mg2+, Fe, Pb, Cd, As, and Ni were analytical techniques/instruments employed. In regard to cations, Na+ has the highest average concentration of 63.0 mg/L, while Mg2+ has the lowest at 2.58 mg/L. Concerning the anions and nutrients, Cl- has the highest mean concentration of 18.7 mg/L, and Fl- has the lowest at 0.50 mg/L. Metalloids were detected in trace amount with Fe displaying the highest mean concentration of 0.077 mg/L whereas Cd and As recorded lowest (0.001 mg/L). The average values for groundwater δ18O and δ2H were - 3.64‰ and - 20.7‰, respectively; the average values for rainwater isotopic composition were - 3.41‰ for δ18O and - 17.4‰ for δ2H. It is believed that natural geological features, particularly biotite granitoid and volcanic flow/subvolcanic rocks from the Birimian Supergroup, significantly influence groundwater mineralisation. Additionally, the impact of anthropogenic activities on water quality, with urban development and agricultural practices, may be attributed to increasing levels of certain contaminants such as Fe, Ni, NO3-, and PO43-. This research contributes to the broader field of hydrological study and provides practical implications for managing and conserving water resources in similar contexts. The innovative combination of isotopic and statistical analyses sets a new standard for future studies in groundwater quality assessment, emphasising the need for comprehensive approaches that consider both geological characteristics and human impacts for sustainable water resource management.