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 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.
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.
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 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.
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.
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 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.
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 influence of tourism development and economic policy uncertainties on environmental sustainability is substantial. Promoting responsible tourism and using sustainable tourism practises, like offering eco-friendly lodging, is a key part of protecting natural habitats and lowering carbon footprints. Hence, this study tries to examine the relationship between tourism development, economic policy uncertainty, renewable energy, and natural resources on the ecological footprint of India during 1990-2022. This study applies a novel dynamic ARDL simulation approach for long-run and short-run analyses. The study also employs frequency-domain causality to check the causal relationship between the variables. The result reveals that tourism has a positive effect on the ecological footprint. Similarly, economic policy uncertainty has a positive and significant effect on the ecological footprint in India during the sample period. Additionally, natural resource rent shows a positive effect on the ecological footprint or deteriorating environmental quality in the short and long run in the sample period. However, renewable energy consumption indicates a negative effect on the ecological footprint. The results reveal that TDI and EPU have rejected the null hypothesis of no Granger cause in the long, medium, and short term. While renewable energy has a causal relationship with ecological footprints in both the long run and medium run, it is imperative for India to adopt measures that facilitate the advancement of sustainable tourism, with a particular focus on promoting environmentally friendly lodging options, enhancing public transportation systems, and implementing effective waste management strategies.
Metal-organic frameworks (MOFs) have emerged as highly promising adsorbents for removing heavy metals from wastewater due to their tunable structures, high surface areas, and exceptional adsorption capacities. This review meticulously examines and summarizes recent advancements in producing and utilizing MOF-based adsorbents for sequestering heavy metal ions from water. It begins by outlining and contrasting commonly employed methods for synthesizing MOFs, such as solvothermal, microwave, electrochemical, ultrasonic, and mechanochemical. Rather than delving into the specifics of adsorption process parameters, the focus shifts to analyzing the adsorption capabilities and underlying mechanisms against critical metal(loid) ions like chromium, arsenic, lead, cadmium, and mercury under various environmental conditions. Additionally, this article discusses strategies to optimize MOF performance, scale-up production, and address environmental implications. The comprehensive review aims to enhance the understanding of MOF-based adsorption for heavy metal remediation and stimulate further research in this critical field. In brief, this review article presents a comprehensive overview of the contemporary information on MOFs as an effective adsorbent and the challenges being faced by these adsorbents for heavy metal mitigation (including stability, cost, environmental issues, and optimization), targeting to develop a vital reference for future MOF research.
Molten salts are the operational fluid for most concentrated solar power (CSP) systems, which has attracted more attention among the scientific community due to the augmentation of their properties with the doping of nanoparticles. Hexagonal boron nitride (h-BN) nanoparticles were dispersed in HITEC molten salt to create a novel nanofluid and evaluate the h-BN nanoparticles' influence on HITEC thermophysical properties. The influence of nanoparticle concentration (0.1, 0.5, and 1wt.%) of h-BN and HITEC was studied in this research. HITEC and nano-enhanced HITEC molten salt (NEHMS) were characterized using energy-dispersive X-ray spectroscopy (EDX), field emission scanning electron microscopy (FESEM), and Fourier transform infrared spectroscopy (FT-IR). Specific heat capacity, latent heat, and melting temperature were assessed using differential scanning calorimetry (DSC). The maximum working temperature was evaluated with thermogravimetric analysis (TGA). The ideal nanoparticle concentration is 0.1 wt.% h-BN, which results in a 27% increase in heat capacity, a 72% increase in latent heat, and a 7% enhancement in thermal stability. The thermal cycling stability test proved the stability of the enhanced thermophysical properties. The material characterization revealed that the samples with improved thermophysical properties have a homogeneous dispersion of nanoparticles with minor nanoparticle agglomeration. The system advisor model (SAM) simulation comparison of the optimum sample with solar salt and HITEC salt revealed that using the optimum sample increases CSP plant efficiency by 0.4% and reduces power costs by 0.13¢/kWh.
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.
Hydrogen (H2) represents a promising avenue for reducing carbon emissions in energy systems. However, achieving its widespread adoption requires more effective and scalable synthesis methods. Herein, we investigated the isothermal carburization process of the MoO3 catalyst. This reaction was carried out at a constant temperature of 700 °C in a 60% CO/He stream, with hold reaction times varying (60-min, 90-min, and 120-min). This investigation was conducted using a micro-reactor Autochem with the aim of enhancing the yield of H2. The study focused on evaluating the chemical reduction and carburization behavior of the MoO3 catalyst through X-ray diffraction (XRD), transmission electron microscopy (TEM), and CHNS elemental analysis. The XRD analysis revealed the formation of carbides, Mo2C, and MoO2, serving as active sites for subsequent H2 production in the thermochemical water splitting (TWS) process. The carburization at a 60-min hold time exhibited enhanced H2 production, generating approximately ~ 6.60 µmol of H2 with a yield of up to ~ 32.90% and a conversion rate of ~ 54.83%. This finding emphasizes the essential role played by the formation of carbides, particularly Mo2C, in the carburization process, contributing significantly to the facilitation of H2 production. These carbides serve as exceptionally active catalytic sites that actively promote the generation of hydrogen. This study underscores that the optimized duration of catalyst exposure is a key factor influencing the successful carburization of MoO3 catalysts. This emphasizes how important carbide species are to increasing H2 efficiency. Additionally, it is noted that carbon formation on the MoO3 active sites can act as a potential poison to the catalysts, leading to rapid deactivation after prolonged exposure to the CO precursor.
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.
Methylene blue (MB) was regarded as a highly toxic and hazardous substance owing to its irreparable hazard and deplorable damage on the ecosystem and the human body. The treatment of this colorant wastewater appeared to be one of the towering challenges in wastewater treatment. In this study, a microbial fuel cell coupled with constructed wetland (CW-MFC) with effective MB elimination and its energy recuperation concurrently based on the incorporation of carbide lime as a substrate in a new copper oxide-loaded on carbon cloth (CuO/CC) cathode system was studied. The crucial influencing parameters were also delved, and the MB degradation and chemical oxygen demand (COD) removal efficiencies were correspondingly incremented by 97.3% and 89.1% with maximum power output up to 74.1 mW m-2 at optimal conditions (0.2 g L-1 carbide lime loading and 500 Ω external resistance). The carbide lime with high calcium ion content was greatly conducive for the enrichment of critical microorganism and metabolic activities. The relative abundances of functional bacteria including Proteobacteria and Actinobacteriota were vividly increased. Moreover, the impressive results obtained in printed ink wastewater treatment with a COD removal efficiency of 81.3% and a maximum power density of 58.2 mW m-2, which showcased the potential application of CW-MFC.
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.