This study marks the first report on the genetic characterization of Penicillium expansum strain capable of mycotoxin production isolated from river water. Situated in Ganagalawanipeta village, Srikakulam, Andhra Pradesh, India, where river water serves as a vital resource, our investigation probed the presence of pathogenic opportunistic fungi adept at mycotoxin synthesis. Over six months, 30 samples were collected to assess their occurrence. This article revolves around the use of morphological traits for Penicillium genus identification. Precise species determination involved PCR analysis using universal primers ITS1 and ITS4, followed by sequence analysis through NCBI-BLASTn and the ITS2 database. The analysis indicated a striking 99.49% genetic similarity to Penicillium expansum isolate MW559596 from CSIR-National Institute of Oceanography, Goa, an Indian isolate, with a resultant 600-base pair fragment. This sequence was officially cataloged as OR536221 in the NCBI GenBank database. Sequence and phylogenetic assessments were conducted to pinpoint the strain and geographical origin. Notably, the ribosomal nuclear ITS region displayed significant inter- and intra-specific divergence, manifested in DNA barcodes and secondary structures established via minimum free energy calculations. These findings provide crucial insights into the genetic diversity and potential mycotoxin production of P. expansum isolates, shedding light on the environmental repercussions and health risks associated with river water contamination from agricultural and aquaculture effluents. This pioneering research advances our understanding of mycotoxin-producing fungi in aquatic environments and underscores the imperative need for water quality monitoring in regions reliant on such water sources for their sustenance and livelihoods.

Modeling the fate and transport of spills in rivers is critical for risk assessment and instantaneous spill response. In this research, a one-dimensional model for instantaneous spills in river systems was built by solving the advection-dispersion equation (ADE) numerically along with the shallow water equations (SWEs) within the MATLAB environment. To run the model, the Ohio River’s well-known accidental spill in 1988 was used as a field case study. The verification process revealed the model’s robustness with very low statistic errors. The mean absolute error (MAE) and root mean squared error (RMSE) relative to the absorbed record were 0.0626 ppm and 0.2255 ppm, respectively. Results showed the spill mass distribution is a function of the longitudinal dispersion coefficient and the mass decay rate. Increasing the longitudinal dispersion coefficient reduces the spill impact widely, for instance after four days from the mass spill the maximum concentration decreased from 0.846789 to 0.486623 ppm, and after five days it decreased from 0.332485 to 0.186094 ppm by increasing the coefficient from 15 to 175 m2/sec. A similar reduction was achieved by increasing the decay rate from 0.8 to 1.2 day-1 (from 0.846789 to 0.254274 ppm and from 0.332485 to 0.0662202 ppm after four and five days, respectively). Thus, field measurements of these two factors must be taken into account to know the spill fate in river systems.

Generally, in India, determining the chlorine and coagulant dosage in a WTP depends on the proficiency of operators, which may lead to overdosing or underdosing of coagulants and chlorine. Nevertheless, the determination of both coagulant and chlorine dosages frequently changes as inlet water quality varies which demands extensive laboratory analyses, leading to prolonged experimentation periods in water treatment plants. So objective of the study is to develop the precise relationship between coagulant dose and chlorine dose in a water treatment plant by using an artificial neural network (ANN). As a result, ANN models were developed to predict chlorine dose using coagulant dose by comparing the performance of the number of ANN models. It has been found that radial basis function neural networks (RBFNN) and generalized regression neural networks (GRNN) modeling provide better prediction. In RBFNN and GRNN modeling, the spread factor is varied from 0.1 to 15 to establish a stable and accurate model with high predictive accuracy. It is observed that the RBFNN model showed good prediction (R2 = 0.999). The application of a soft computing model for defining doses of coagulant and chlorine that are inextricably linked at a Water treatment plant (WTP) will be highly beneficial for WTP Managers.

The two biggest environmental issues the world is currently dealing with are global warming and climate change. Minimizing energy consumption will help to cut down on greenhouse gas emissions, which is our responsibility. Companies choose ‘Carbon Footprint’ as a tool to calculate greenhouse gas emissions to show the impact of their activities on the environment. The techniques and procedures used in the analysis of carbon footprints are the primary focus of this study. Several criteria for evaluating carbon footprints were compared to one another to uncover parallels, variances, and deficiencies. Carbon footprints of companies and items were analyzed, and their objectives, ideas, topics of inquiry, calculation techniques, data choices, and additional elements were investigated. Standards for both organizations (ISO14064 and the GHG protocol) and products were compared and contrasted to arrive at accurate carbon footprint estimates. The most important aspects of a carbon footprint and assessment criterion are the research of GHG, system settings, measurement and carbon footprint, date, and treatment of individual emissions. Especially true for commercial enterprises and consumer goods. Guidelines have been produced for these challenges based on valuation criteria that have been used up to this point; nonetheless, they should be enhanced. This study highlights the need to formulate policies to reduce greenhouse gas emissions.

Drinking water contamination by microplastic particles is a global concern that is becoming increasingly common due to consumer abuse, and we use laser fractionation spectroscopy to examine what microplastic particles in water packaging can do. Several types of bottled water were sampled at several manufacturing facilities in Baghdad. The presence of the measured micropolymer species in water was immediately classified and detected using a laser production resolution spectrometer as well as signal and plasma scattering spectra, various MP polymers “polyethylene terephthalate, polystyrene, polypropylene, polyethylene, and polyvinyl chloride” are five polymers that were successfully detected in drinking water to validate the ability to identify health risk factors based on potential environmental risk index (RI) and potential environmental risk factors (Tin), the results are calculated to show that risk predicates have evolved over a decade depending on the risk factors. To do. The smallest particle was 20 microns and the largest particle was 63.4 microns. Microplastics were detected in 5 out of 10 samples, PET in 4 samples, PS and PP in 2 samples, and PVC in sample 1, the most common polymer in bottled water is polyethylene. The average C/H ratios of the five samples were PE (1.76), PET (1.21), PS (1.52), PP (1.23), and PVC (0.99), on average, the measured trends of C/H values were [PE greater than PS], [PP greater than PET], and [PVC greater than PET]. According to our results, the integration of LIBS technology provides a fast and efficient way to detect microplastics. It has a high resolution of fine particles, allowing the detection of very small particles associated with various adverse effects on human health. The feasibility study for water bottling was approved, and the WHO water quality criteria were confirmed. As a result, we will undertake a thorough analysis of the best water bottling quality. In this study, the initial LIBS signals of several samples were used to completely detect microplastics. Microplastics in bottled water samples have been detected and quantified using LIBS spectroscopy techniques with Ecological Potential Ecological Risk. Analytical technology is used to investigate sources, perform research, and collect relevant data, worldwide reports, and permitted statistics to deliver crucial insights and recommendations.Water samples were obtained from several locations throughout Baghdad. At the source, 2 liters of water were obtained in plastic bottles for each sample, for a total of 10 samples. Each sample is owned by the factories that supplied it.

Amidst the shifting tides of the educational landscape, this research article embarks on a transformative journey delving into the fusion of theoretical principles and pragmatic implementations within the realm of Graph Machine Learning (GML), particularly accentuated within the sphere of nature, environment, and pollution technology. GML emerges as a potent and indispensable tool, adeptly leveraging the intrinsic interconnectedness embedded within environmental datasets. Its application extends far beyond mere analysis towards the profound ability to forecast ecological patterns, prescribe sustainable interventions, and tailor pollution mitigation strategies with precision and efficacy. This article does not merely scratch the surface of GML’s applications but dives deep into its tangible implementations, unraveling its potential to revolutionize environmental science and pollution technology. It endeavors to bridge the gap between theory and practice, weaving together relevant ecological theories and empirical evidence that underpin the theoretical foundations supporting GML’s practical utility in environmental domains. By synthesizing theoretical insights with real-world applications, this research elucidates the profound transformative potential of GML, paving the way for proactive and data-driven approaches toward addressing pressing environmental challenges. In essence, this harmonization of theory and application catalyzes advancing the adoption of GML in environmental science and pollution technology. It not only illuminates the path towards sustainable practices but also lays the groundwork for fostering a holistic understanding of our ecosystem. Through this integration, GML emerges as a beacon guiding us toward a future where environmental stewardship is informed by data-driven insights, leading to more effective and sustainable solutions for the benefit of our planet and future generations.

The indigenous knowledge of the Dayak Paramasan in Indonesia holds the potential for environmental sustainability. This study aims to assess an environmental education framework grounded in the local wisdom of the Paramasan Dayak tribe. A survey was conducted among 300 individuals, including traditional leaders and members of indigenous communities residing in the Paramasan Subdistrict, Indonesia. Data collection occurred from May 2023 to July 2023 and was analyzed using Structural Equation Modelling (SEM). The findings indicate a significant association between indigenous values, local expertise, and community cohesion concerning environmental education. Local wisdom includes local skills, values, and community solidarity, which are crucial for environmental education. Local skills, like farming and hunting, have a significant impact on environmental protection. Passing down knowledge to younger generations needs improvement. Limited local resources create a gap between generations, but some believe traditional leaders can safeguard nature without formal education. Further exploration of implementing environmental education models within school settings will offer valuable insights for Indigenous communities and society, fostering environmentally conscious behaviors.

In this study, we synthesized cobalt-doped molybdenum supported on silica (Co/MS) nanocomposites with varying concentrations of cobalt (1, 5, 10, 15, and 20 wt%) using the sol-gel method. We investigated their physico-chemical properties, photocatalytic activity, and antimicrobial efficacy. The synthesized nanocomposites were characterized using a range of techniques, including X-ray powder diffraction (XRD) to determine crystal structure, UV-vis spectroscopy for optical properties, Fourier transform infrared spectroscopy (FT-IR) for functional group analysis, and scanning electron microscopy coupled with energy-dispersive X-ray microanalysis (SEM-EDX) for morphological and elemental composition analysis. The photocatalytic performance of these catalysts was assessed by their ability to degrade organic dyes, specifically methyl orange and methylene blue, under visible light irradiation. Our results demonstrated that the photocatalytic efficiency increased with higher cobalt content, with the 20 wt% Co/MS nanocomposite showing the highest degradation rates. Additionally, we evaluated the antibacterial activity of the nanocomposites against a range of microorganisms, including Gram-positive and Gram-negative bacteria, as well as fungal species. The 20 wt% Co/MS nanocomposite exhibited superior antimicrobial activity compared to the other samples, indicating its potential for applications in environmental remediation and antimicrobial treatments.

The most significant air pollutant is particulate matter of less than 10 microns (PM10), followed by ozone (O3) during the monitoring period from 2006 to 2022 in Chiangmai. The association between ambient pollutants and climate conditions in Chiangmai was assessed using regression analysis and analysis of variance (ANOVA). The ANOVA analysis indicated that the average temperature was associated significantly with the nitrogen dioxide (NO2) concentration in the ambient, but the average rainfall volume was associated significantly with most pollutants except only sulfur dioxide (SO2). From the prediction models, the rise in average temperature affected to increase in the concentrations of PM10 and O3. Interestingly, the increase in rainfall will be advantageous to compromise the severity of all pollutants. Meanwhile, on hotter days should be careful of the rise of PM10 and O3 concentrations. Therefore, the vital meteorological variables associated with air pollution are very useful for forecasting the harmful and severity level of each air pollutant.

The high rate of generation of plastic waste in the country and the fact that all other means of Municipal Plastic Waste (MPW) management techniques had failed leading to the requirement of efficient and alternative disposal technique-depolymerization. The technique involves heating the polymeric waste at an elevated temperature in an inert environment to produce condensable, non-condensable, hydrocarbon and biochar. The plastic waste was collected at the Ilokun dumpsite in Ado-Ekiti, southwest Nigeria. Each component of the waste samples was depolymerized in a batch reactor without the use of a catalyst and with the addition of 10 g of activated carbon (AC) and calcium oxide (CaO) as catalysts. The liquid fuels which were produced between the temperature range of 219 and 232 were blended with standard fuel. Fuel samples with conventional diesel and depolymerized plastic diesel were characterized based on ASTM standards. The results of the proximate and ultimate analysis indicated that percentage moisture content ranges from 0.00-0.18%, volatile matter ranges between 96.66-99.75% and percentage ash content ranges from 0.13-3.03%. Fixed carbon ranges from 0.004-0.31% while the Gross Heating Value (GHV) ranges from 42.66-45.87 MJ/kg. The CHONS analyzer indicated the percentage of carbon, hydrogen, oxygen, nitrogen, and sulfur content range 81.64-85.51%, 12-31-18.04%, 0.00-1.51%, 0.00-0.73%, and 0.10- 0.97% respectively. The results of the physiochemical properties of the samples show that the density, API gravity, Kinematic viscosity and Flash point vary from 0.76-0.83 (g/cm3), 38.98-54.68, 17-2.80 (cm2/s) and 50.0-70.0 (°C) respectively while Cloud point, Pour point, Fire point and Cetane index range from -20-15.0 (°C), -23-7 (°C), 61.0-79.0 (°C) and 38.50-47.0. The pH values of the liquid fuel samples vary from 6.60-3.30. The overall results of the characterization indicated the fuel samples have proximity to the properties of the conventional diesel following the ASTM D975, ASTM D4737, ASTM D1298, ASTM D445, ASTM D2709, and ASTM D482 standards. The depolymerized polymeric waste is sustainable, with a low cost of production. Hence a good substitute as an alternative fuel and means of wealth creation from waste.