The discharge of large quantities of organic dyes into the environment causes significant harm to humans and the environment. Thus, there is an urgent need to develop cost-effective adsorbents for removing these dyes. In the present study, the synthesis of activated carbon (AC) derived from mixed fish scale waste using KOH activation was investigated for Congo red (CR) dye removal. The finding shows that the obtained biocarbon has a fixed carbon of 42.9% with a crystallinity index of 15.01%. N2 adsorption-desorption isotherm was found to be type IV, signifying mesoporous structure with a surface area and total pore volume of 150.049 m2 g-1 and 0.119 cm3.g-1. Batch adsorption was carried out by various adsorbent doses, initial concentration, contact time, and pH to comprehend the effect of operating parameters on its removal efficacy. The isotherm studies fitted well for Freundlich with an R2 of 0.99%. Adsorption kinetics was best fitted by the pseudo-second-order model and thermodynamic studies revealed the adsorption process to be exothermic and spontaneous. The efficiency of AC was also studied by an amount of sorption and desorption cycles which showed its potential for reusability up to the sixth cycle. Thus, the findings suggest that activated carbon derived from mixed fish scale waste is a promising adsorbent for removing Congo red dye from aqueous solutions.

Global warming and its consequences have heightened the urgency of reducing emissions of carbon dioxide globally. The concern arises from countries’ relentless efforts to achieve economic development at the expense of the environment. In this context, the paper examines the Environmental Kuznets Curve (EKC) hypothesis at the world level using carbon emission as an indicator of environmental degradation. The EKC hypothesis postulates an inverted U-shaped curve between economic development and environmental degradation; degrading environmental quality at the initial stages of development and, after a threshold level, environmental degradation lowers. The study investigates the validity of the EKC hypothesis for carbon emission with an analysis of 158 countries in the world, with population, urbanization, forest cover, and tourist inflow as the control variables. The study is based on secondary data collected from the World Bank. A regression analysis is used for the study. To ensure environmental sustainability, it is important to identify the determinants of carbon emissions across countries with varying levels of economic development. The findings of the study support the hypothesized inverse U-shaped association between Gross Domestic Product per capita and carbon emission per capita at the world level. Out of the four control variables, urbanization and tourist inflow were found statistically significant. Urbanization was positively correlated with carbon emission per capita while forest area was negatively correlated. Carbon emission per capita initially increases with rising GDP per capita and declines after GDP per capita reaches a certain level. The estimated turning point of GDP per capita occurs at a high level and therefore, most of the countries are anticipated to emit carbon dioxide.

Precipitation is a fundamental variable that is widely used in the organization of water resources and has a great influence on hydrological processes and ecological assessment. This study investigated the quantitative effect of monthly precipitation on surface water area (denoted by the Modified Normalized Difference Water Index, MNDWI), vegetation area (denoted by Normalized Difference Vegetation Index, NDVI), and potential evapotranspiration (PET) during two years (2018 and 2021) in the city of Baghdad, Iraq. Using the Thornthwaite aridity index, the annual aridity was first assessed to quantify the climate category of these years. The result shows that they were semi-arid and very arid, respectively. The empirical relationships between precipitation and areas of MNDWI and NDVI, and between rainfall and PET, were also examined. Due to less precipitation in 2021, no relationship was found in arid climates, while in 2018 for semi-arid climates, precipitation had a positive non-linear correlation with MNDWI and NDVI areas and a negative correlation with PET.

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.