The response patterns of microbial functional genes involved in biogeochemical cycles to cadaver decay is a central topic of recent environmental sciences. However, the response mechanisms and pathways of the functional genes associated with the carbon (C) and nitrogen (N) cycling to cadaveric substances such as cadaverine and putrescine remain unclear. This study explored the variation of functional genes associated with C fixation, C degradation and N cycling and their influencing factors under cadaverine, putrescine and mixed treatments. Our results showed only putrescine significantly increased the alpha diversity of C fixation genes, while reducing the alpha diversity of N cycling genes in sediment. For the C cycling, the mixed treatment significantly decreased the total abundance of reductive acetyl-CoA pathway genes (i.e., acsB and acsE) and lig gene linked to lignin degradation in water, while only significantly increasing the hydroxypropionate-hydroxybutylate cycle (i.e., accA) gene abundance in sediment. For the N cycling, mixed treatment significantly decreased the abundance of the nitrification (i.e., amoB), denitrification (i.e., nirS3) genes in water and the assimilation pathway gene (i.e., gdhA) in sediment. Environmental factors (i.e., total carbon and total nitrogen) were all negatively associated with the genes of C and N cycling. Therefore, cadaverine and putrescine exposure may inhibit the pathway in C fixation and N cycling, while promoting C degradation. These findings can offer some new insight for the management of amine pollution caused by animal cadavers.
The two limiting factors for lentil protein utilization are water solubility and digestibility. In this study, we utilized two non-thermal techniques: (1) protein complexation of lentil and casein proteins using the pH-shifting method and (2) protein conjugation with trehalose to produce trehalose-conjugated lentil-casein protein complexes (T-CPs) with enhanced water solubility and digestibility. The protein structure of the T-CPs was analyzed for secondary protein structure, conformation protein, and tertiary protein structure using Fourier-transform infrared, UV, and fluorescence spectroscopies, respectively. The surface hydrophobicity and surface charge of T-CPs solution at pH 7.0 changed significantly (P water solubility and digestibility of T-CPs increased significantly (P water solubility and digestibility.
The approach of combining cost-effective nanopore sequencing and emerging environmental DNA (eDNA) metabarcoding could prove to be a promising tool for biodiversity documentation, especially in Malaysia. Given the substantial funding constraints in recent years, especially in relation to the country's biodiversity, many researchers have been limited to conduct restricted research without extended monitoring periods, potentially hindering comprehensive surveys and could compromise the conservation efforts. Therefore, the present study aimed to evaluate the application of eDNA metabarcoding on freshwater fish using short reads generated through nanopore sequencing. This assessment focused on species detection in three selected rivers within the Endau Rompin Landscape in Malaysia. Additionally, the study compared levels of species detection between eDNA metabarcoding and conventional sampling methods, examined the effectiveness of primer choice, and applied both metabarcoding and shotgun sequencing to the eDNA approach. We successfully identified a total of 22 and 71 species with an identification threshold of >97% and >90%, respectively, through the MinION platform. The eDNA metabarcoding approach detected over 13% more freshwater fish species than when the conventional method was used. Notably, the distinction in freshwater fish detection between eDNA primers for 12S rRNA and cytochrome oxidase I was insignificant. The cost for eDNA metabarcoding proved to be more effective compared to conventional sampling with cost reduction at 33.4%. With favourable cost-effectiveness and increased species detection, eDNA metabarcoding could complement existing methods, enhance holistic diversity documentation for targeted habitats and facilitate effective conservation planning.
Several activities such as aquaculture, human and feedstock therapies can directly release antibiotics into water. Due to high stability, low hydrolysis and non-biodegradation, they can accumulate in the aqueous environment and transport to aquatic species. Here, we synthesized amine-functionalized porous carbons (ANC) by a direct-pyrolysis process of NH2-MIL-53(Al) as a sacrificial template at between 600 and 900 °C and utilized them to eliminate chloramphenicol antibiotic from water. The NH2-MIL-53(Al)-derived porous carbons obtained high surface areas (304.7-1600 m2 g-1) and chloramphenicol adsorption capacities (148.3-261.5 mg g-1). Several factors such as hydrogen bonding, Yoshida hydrogen bonding, and π-π interaction, hydrophobic interaction possibly controlled adsorption mechanisms. The ANC800 could be reused four cycles along with high stability in structure. As a result, NH2-MIL-53(Al)-derived porous carbons are recommended as recyclable and efficient adsorbents to the treatment of antibiotics in water.
The temperature of surface and epilimnetic waters, closely related to regional air temperatures, responds quickly and directly to climatic changes. As a result, lake surface temperature (LSWT) can be considered an effective indicator of climate change. In this study, we reconstructed and investigated historical and future LSWT across different scenarios for over 80 major lakes in mainland Southeast Asia (SEA), an ecologically diverse region vulnerable to climate impacts. Five different predicting models, incorporating statistical, machine and deep learning approaches, were trained and validated using ERA5 and CHIRPS climatic feature datasets as predictors and 8-day MODIS-derived LSWT from 2000 to 2020 as reference dataset. Best performing model was then applied to predict both historical (1986-2020) and future (2020-2100) LSWT for SEA lakes, utilizing downscaled climatic CORDEX-SEA feature data and multiple Representative Concentration Pathway (RCP). The analysis uncovered historical and future thermal dynamics and long-term trends for both daytime and nighttime LSWT. Among 5 models, XGboost results the most performant (NSE 0.85, RMSE 1.14 °C, MAE 0.69 °C, MBE -0.08 °C) and it has been used for historical reconstruction and future LSWT prediction. The historical analysis revealed a warming trend in SEA lakes, with daytime LSWT increasing at a rate of +0.18 °C/decade and nighttime LSWT at +0.13 °C/decade over the past three decades. These trends appeared of smaller magnitude compared to global estimates of LSWT change rates and less pronounced than concurrent air temperature and LSWT increases in neighbouring regions. Projections under various RCP scenarios indicated continued LSWT warming. Daytime LSWT is projected to increase at varying rates per decade: +0.03 °C under RCP2.6, +0.14 °C under RCP4.5, and +0.29 °C under RCP8.5. Similarly, nighttime LSWT projections under these scenarios are: +0.03 °C, +0.10 °C, and +0.16 °C per decade, respectively. The most optimistic scenario predicted marginal increases of +0.38 °C on average, while the most pessimistic scenario indicated an average LSWT increase of +2.29 °C by end of the century. This study highlights the relevance of LSWT as a climate change indicator in major SEA's freshwater ecosystems. The integration of satellite-derived LSWT, historical and projected climate data into data-driven modelling has enabled new and a more nuanced understanding of LSWT dynamics in relation to climate throughout the entire SEA region.
Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.
This study employs an artificial neural network optimization algorithm, enhanced with a Genetic Algorithm-Back Propagation (GA-BP) network, to assess the service quality of urban water bodies and green spaces, aiming to promote healthy urban environments. From an initial set of 95 variables, 29 key variables were selected, including 17 input variables, such as water and green space area, population size, and urbanization rate, six hidden layer neurons, such as patch number, patch density, and average patch size, and one output variable for the comprehensive value of blue-green landscape quality. The results indicate that the GA-BP network achieves an average relative error of 0.94772%, which is superior to the 1.5988% of the traditional BP network. Moreover, it boasts a prediction accuracy of 90% for the comprehensive value of landscape quality from 2015 to 2022, significantly outperforming the BP network's approximate 70% accuracy. This method enhances the accuracy of landscape quality assessment but also aids in identifying crucial factors influencing quality. It provides scientific and objective guidance for future urban landscape structure and layout, contributing to high-quality urban development and the creation of exemplary living areas.
Frequent detection of sulfonamides (SAs) pharmaceuticals in wastewater has necessitated the discovery of suitable technology for their sustainable remediation. Adsorption has been widely investigated due to its effectiveness, simplicity, and availability of various adsorbent materials from natural and artificial sources. This review highlighted the potentials of carbon-based adsorbents derived from agricultural wastes such as lignocellulose, biochar, activated carbon, carbon nanotubes graphene materials as well as organic polymers such as chitosan, molecularly imprinted polymers, metal, and covalent frameworks for SAs removal from wastewater. The promising features of these materials including higher porosity, rich carbon-content, robustness, good stability as well as ease of modification have been emphasized. Thus, the materials have demonstrated excellent performance towards the SAs removal, attributed to their porous nature that provided sufficient active sites for the adsorption of SAs molecules. The modification of physico-chemical features of the materials have been discussed as efficient means for enhancing their adsorption and reusable performance. The article also proposed various interactive mechanisms for the SAs adsorption. Lastly, the prospects and challenges have been highlighted to expand the knowledge gap on the application of the materials for the sustainable removal of the SAs.
Arundo donax L. is investigated in this study as a suitable reinforcing agent for PLA/PP waste blend 3D printing filament. To improve the compatibility of the fibre and polymer, the Arundo fibre was chemically modified using alkali and silane treatment. Untreated and treated fibres were extruded with Polymer blends before being 3D printed. Effect of chemical treatment on thermal, mechanical, and morphological properties of the composites was investigated. The tensile, Izod impact, and water absorption of the 3D printed specimens were also tested. The Alkali treated (ALK) and combination of alkali and silane treatment (SLN) composites displayed good results. Tensile strength and modulus of the materials increased, as well as their maintained stability in the Izod impact test, demonstrating that the incorporation of ArF did not result in a loss in performance. SEM examination supported these findings by confirming the creation of beneficial interfacial contacts between the matrix and fibre components, as demonstrated by the lack of void between the matrix and the fibre surface. Furthermore, the alkali treatment of the ArF resulted in a considerable reduction in water absorption inside the biocomposite, with a 64% reduction seen in ALK composite comparison to the untreated composite (Un). After the 43-day assessment period.
Thin film composite (TFC) reverse osmosis (RO) membrane shows good promise for treating wastewater containing endocrine disrupting chemical (EDC) pollutants. The incorporation of functional materials with exceptional structural and physico-chemical properties offers opportunities for the membranes preparation with enhanced permselectivity and better antifouling properties. The present study aims to improve the EDC removal efficiency of TFC RO membrane using two-dimensional titania nanosheets (TNS). RO membrane was prepared by incorporating TNS in the dense layer of polyamide (PA) layer to form thin film nanocomposite (TFN) membrane. The TNS loading was varied and the influences on membrane morphology, surface hydrophilicity, surface charge, as well as water permeability and rejection of EDC were investigated. The results revealed that the inclusion of TNS in the membrane resulted in the increase of water permeability and EDC rejection. When treating the mixture of bisphenol A (BPA) and caffeine at 100 ppm feed concentration, the TFN membrane incorporated with 0.05% TNS achieved water permeability of 1.45 L/m2·h·bar, which was 38.6% higher than that of unmodified TFC membrane, while maintaining satisfactory rejection of >97%. The enhancement of water permeability for TFN membrane can be attributed to their hydrophilic surface and unique nanochannel structure created by the nanoscale interlayer spacing via staking of TiO2 nanosheets. Furthermore, the 0.05TFN membrane exhibited excellent fouling resistance towards BPA and caffeine pollutants with almost 100% flux recovery for three cycles of operations.
In this study, the synthesis and experimental theoretical evaluation of a new chitosan/alginate/hydrozyapatite nanocomposite doped with Mn2 and Fe2O3 for Cr removal was reported. The physicochemical properties of the obtained materials were analyzed using the following methods: SEM-EDX, XRD, FTIR, XPS, pH drift measurements, and thermal analysis. The adsorption properties were estimated based on equilibrium and adsorption kinetics measurements. The Langmuir, Freundlich and Temkin isotherms were applied to analyze the equilibrium data. The thermodynamic analysis of adsorption isotherms was performed. A number of equations and kinetic models were used to describe the adsorption rate data, including pseudo-first (PFOE) and pseudo-second (PSOE) order kinetic equations. The obtained test results show that the synthesized biomaterial, compared to pure chitosan, is characterized by greater resistance to high temperatures. Moreover, this biomaterial had excellent adsorption properties. For the adsorption of Cr (VI), the equilibrium state was reached after 120 min, and the sorption capacity was 455.9 mg/g. In addition, DFT calculations and NCI analyses were performed to get more light on the adsorption mechanism of Cr (VI) on the prepared biocomposite.
This present work targets the production of an eco-friendly adsorbent (hereinafter KA/CEL) from kaolin clay functionalized with cellulose extract obtained from peanut shells. The adsorbents were used for decolorization of two different types of organic dyes (cationic: methylene blue, MB; anionic: Congo red, CR) from an aqueous environment. Several analytical methods, including Brunauer-Emmett-Teller (surface properties), Fourier Transforms infrared (functionality), scanning electron microscope, Energy dispersive X-Ray (morphology), and pHpzc test (surface charge), were used to attain the physicochemical characteristics of KA/CEL. The Box-Behnken Design (BBD) was applied to determine the crucial factors affecting adsorption performance. These included cellulose loading at 25 %, an adsorbent dose of 0.06 g, solution pH set at 10 for MB and 7 for CR, a temperature of 45 °C, and contact times of 12.5 min for MB and 20 min for CR dye. The adsorption data exhibited better agreement with the pseudo-second-order kinetic and Freundlich models. The Langmuir model estimated the monolayer capacity to be 291.5 mg/g for MB and 130.7 mg/g for CR at a temperature of 45 °C. This study's pivotal finding underscores the promising potential of KA/CEL as an effective adsorbent for treating wastewater contaminated with organic dyes.
Municipal wastewater is ubiquitously laden with myriad pollutants discharged primarily from a combination of domestic and industrial activities. These heterogeneous pollutants are threating the natural environments when the traditional activated sludge system fails sporadically to reduce the pollutants' toxicities. Besides, the activated sludge system is very energy intensive, bringing conundrums for decarbonization. This research endeavoured to employ Chlorella vulgaris sp. In converting pollutants from municipal wastewater into hydrogen via alternate light and dark fermentative process. The microalgae in attached form onto 1 cm3 of polyurethane foam cubes were adopted in optimizing light intensity and photoperiod during the light exposure duration. The highest hydrogen production was recorded at 52 mL amidst the synergistic light intensity and photoperiod of 200 μmolm-2s-1 and 12:12 h (light:dark h), respectively. At this lighting condition, the removals of chemical oxygen demand (COD) and ammoniacal nitrogen were both achieved at about 80%. The sustainability of microalgal fermentative performances was verified in recyclability study using similar immobilization support material. There were negligible diminishments of hydrogen production as well as both COD and ammoniacal nitrogen removals after five cycles, heralding inconsequential microalgal cells' washout from the polyurethane support when replacing the municipal wastewater medium at each cycle. The collected dataset was finally modelled into enhanced Monod equation aided by Python software tool of machine learning. The derived model was capable to predict the performances of microalgae to execute the fermentative process in producing hydrogen while subsisting municipal wastewater at arbitrary photoperiod. The enhanced model had a best fitting of R2 of 0.9857 as validated using an independent dataset. Concisely, the outcomes had contributed towards the advancement of municipal wastewater treatment via microalgal fermentative process in producing green hydrogen as a clean energy source to decarbonize the wastewater treatment facilities.
α-Mangostin is the most abundant compound contained in the mangostin (Garcinia mangostana L.) plant which have been developed and proven to have many promising pharmacological effects. However, the low water solubility of α-mangostin causes limitations in its development in clinical purpose. To increase the solubility of a compound, a method currently being developed is to make drug inclusion complexes using cyclodextrins. This research aimed to use in silico techniques namely molecular docking study and molecular dynamics simulation to explore the molecular mechanism and stability of the encapsulation of α-mangostin using cyclodextrins. Two types of cyclodextrins were used including β-cyclodextrin and 2-hydroxypropyl-β-cyclodextrin docked against α-mangostin. From the molecular docking results, it shows that the α-mangostin complex with 2-hydroxypropyl-β-cyclodextrin provides the lowest binding energy value of -7.99 Kcal/mol compared to β-cyclodextrin value of -6.14 Kcal/mol. The α-mangostin complex with 2-hydroxypropyl-β-cyclodextrin also showed good stability based on molecular dynamics simulation during 100 ns. From molecular motion, RDF, Rg, SASA, density, total energy analyzes, this complex shows increased solubility in water and provided good stability. This indicates that the encapsulation of α-mangostin with 2-hydroxypropyl-β-cyclodextrin can increase the solubility of the α-mangostin.Communicated by Ramaswamy H. Sarma.
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.
In this study, strains producing feruloyl esterase were screened by Oxford Cup clear zones method and by evaluating the ability to decompose hydroxycinnamoyl esters. The strain was identified by 16S rDNA molecular biology. The contents of dietary fiber, reducing sugar, water-extractable arabinoxylans, phytic acid, total phenolics, total flavonoid, phenolic compounds composition, microstructure and antioxidant activity in bran before and after fermentation were studied. Eight strains producing feruloyl esterase were screened, among which strain P1 had the strongest ability to decompose hydroxycinnamoyl esters. The strain was identified and named L. fermentum NB02. Compared with unfermented bran, fermented bran exhibited higher contents of soluble dietary fiber, reducing sugar, water-extractable arabinoxylans, total phenolics, total flavonoid, and lower insoluble dietary fiber and phytic acid content. The dense surface structure of bran was destroyed, forming a porous structure. The release of phenolic compounds increased significantly. L. fermentum NB02 fermentation improved the antioxidant capacity of bran.
Metal-organic framework (MOF)--based composites have received significant attention in a variety of applications, including pollutant adsorption processes. The current investigation was designed to model, forecast, and optimize heavy metal (Cu2+) removal from wastewater using a MOF nanocomposite. This work has been modeled by response surface methodology (RSM) and artificial neural network (ANN) algorithms. In addition, the optimization of the mentioned factors has been performed through the RSM method to find the optimal conditions. The findings show that RSM and ANN can accurately forecast the adsorption process's the Cu2+ removal efficiency (RE). The maximum values of RE are achieved at the highest value of time (150 min), the highest value of adsorbent dosage (0.008 g), and the highest value of pH (=6). The R2 values obtained were 0.9995, 0.9992, and 0.9996 for ANN modeling of adsorption capacity based on different adsorbent dosages, Cu2+ solution pHs, and different ion concentrations, respectively. The ANN demonstrated a high level of accuracy in predicting the local minima of the graph. In addition, the RSM optimization results showed that the optimum mode for RE occurred at an adsorbent dosage value of 0.007 g and a time value of 144.229 min.
Matched MeSH terms: Water Pollutants, Chemical*; Waste Water
In aquatic environments, Vibrio and cyanobacteria establish varying relationships influenced by environmental factors. To investigate their association, this study spanned 5 months at a local shrimp farm, covering the shrimp larvae stocking cycle until harvesting. A total of 32 samples were collected from pond A (n = 6), pond B (n = 6), effluent (n = 10), and influent (n = 10). Vibrio species and cyanobacteria density were observed, and canonical correspondence analysis (CCA) assessed their correlation. CCA revealed a minor correlation (p = 0.847, 0.255, 0.288, and 0.304) between Vibrio and cyanobacteria in pond A, pond B, effluent, and influent water, respectively. Notably, Vibrio showed a stronger correlation with pH (6.14-7.64), while cyanobacteria correlated with pH, salinity (17.4-24 ppt), and temperature (30.8-31.5 °C), with salinity as the most influential factor. This suggests that factors beyond cyanobacteria influence Vibrio survival. Future research could explore species-specific relationships, regional dynamics, and multidimensional landscapes to better understand Vibrio-cyanobacteria connections. Managing water parameters may prove more efficient in controlling vibriosis in shrimp farms than targeting cyanobacterial populations.
Organic pollution continues to be an important worldwide obstacle for tackling health and environmental concerns that require ongoing and prompt response. To identify the LAB content levels as molecular indicators for sewage pollution, surface sediments had obtained from the South region of Malaysia. The origins of the LABs were identified using gas chromatography-mass spectrometry (GC-MS). ANOVA and a Pearson correlation coefficient at p
Bioelectrochemical sensors for environment monitoring have the potential to provide facility operators with real-time data, allowing for better and more timely decision-making regarding water and wastewater treatment. To assess the robustness and sensitivity of the Sentry™ biosensor in local conditions, it was tested in Malaysia using domestically available wastewater. The study objectives included (1) enrich the biosensor locally, (2) operate and test the biosensor with local domestic wastewater, and (3) determine the biosensor's responsiveness to model pollutants through pollutant spike and immersion test as well as response to absence of wastewater. Lab-scale operation shows the biosensor was successfully enriched with (1) local University Kebangsaan Malaysia's, microbial community strain collection and (2) local municipal wastewater microflora, operated for more than 50 days with a stable yet responsive carbon consumption rate (CCR) signal. Meanwhile, two independent biosensors were also enriched and operated in Indah Water Research Centre's crude sewage holding tank, showing a stable response to the wastewater. Next, a pilot scale setup was constructed to test the enriched biosensors for the spiked-pollutant test. The biosensors showed a proportional CCR response (pollutant presence detected) towards several organic compounds in the sewage, including ethanol, chicken blood, and dilution of tested sewage but less to curry powder, methanol, and isopropanol. Conversely, there was no significant response (pollutant presence not detected) towards hexane, Congo red, engine oil, and paint, which may be due to their non-biodegradability and/or insoluble nature. Additionally, the biosensors were exposed to air for 6 h to assess their robustness towards aerobic shock with a positive result. Overall, the study suggested that the biosensor could be a powerful monitoring tool, given its responsiveness towards organic compounds in sewage under normal conditions.