Global NEST Journal

In coastal lagoons, eutrophication is a significant ecological and environmental problem. The majority of the pollution and deterioration problems that coastal zones and their ecosystems face are due to human influence through urbanization and industrialization. In this research, evaluations of water quality were made throughout a few lagoons in India during both the wet and dry seasons to establish their suitability for harboring aquatic life and the impact of seasonality on their quality. In order to reduce the effect of environmental influences on sensor based on nanoparticle sensitive film, an enhanced deep Back Propagation (BP) neural system was developed. The incompatibility between high performance and low calculation time was typically found in today’s modern sensor compensation techniques. Moreover, higher recognition errors were always the outcome of weak self-learning abilities. To solve these problems, a 16-layer deep BP neural network model was developed after hyper parameter searching. In Addition, the stochastic gradient descent (SGD) method and the mini-batch method were employed to achieve a proper balance between model performance and training time difficulties. The enhanced deep BP neural network was a great compensation solution for the sensor used in complicated environments due to its self-learning and self-adaptability. The proposed method is named as Imp_BPNN_SGD is analyzed in terms of various parameters and found that it achieves 35.6% of RMSE, 21.6% of MAE, 19.6% of RAE and 97.2% of D2

Urbanized areas groundwater vulnerability and contamination are serious issues that require due attention. There are other models, notably the DRASTIC models employed to assess the vulnerability of groundwater. In a modified version of the DRASTIC model known as anthropogenic influence was incorporated into DRASTICA as a model parameter. The research used a cutting-edge methodology to define the anthropogenic employing satellite data of nighttime lighting to have an impact using human settlements as a stand-in and the Palani Taluk's surrounding land use/landcover. The spatial integration of various parameter maps was done using a geographic information system. According to data on groundwater vulnerability to pollution, around 29.98% of the area is in a very high vulnerability zone, followed by 23.68% of area covers high vulnerability, 25.18% comes under the range of moderate vulnerability, 12.14% covers the area in the zones of low vulnerability, and 8.97% area covers the total area in the ranges of very low vulnerability. Utilizing the amount of nitrate in ground water, the findings were confirmed. It was determined that in an urban environment, the suggested DRASTICA model achieved enhanced than the conservative DRASTIC model. The classifications are as follows: extremely high, high, medium, and low. Very high Vulnerability zones account for 214 values in Palani taluk, the research region, whereas very low vulnerability zones account for 3 values in Palani taluk. Urbanization index showed that anthropogenic impact and the depth of the water table had a substantial impact on groundwater susceptibility to contamination, indicating that manmade effect must be carefully taken into consideration in such studies. This study's modified-DRASTIC/DRASTICA model will aid in better classifying groundwater sensitive areas to contamination where manmade pollution is high, especially in and near metropolitan centers. The groundwater susceptibility possible map that is produced can be more successfully used as the main tool for managing, organizing, and safeguarding groundwater resources. Low nitrate concentrations are found in forests and arid terrain, whereas high nitrate values are found in urban areas. The majority of the Palani taluk has low to moderate nitrate concentrations. The Melkaraipatty and Thalaiyuthu Palani Taluk's urban areas are considered by high sensitivity because of low water levels and strong manmade effect.

This study utilized a series of experimental tests, including the Atterberg limit, standard Proctor, permeability, and direct shear tests. Three composites were investigated: 85% fly ash-10% bentonite (V1), 90% fly ash-5% bentonite (V2), and 95% fly ash-0% bentonite (V3). All composites contained 5% coconut fiber. The plasticity index (PI) increased significantly from 19.10% to 22.15%, with a change in bentonite content from 0% to 10%. All composite met the landfill liner plasticity index standards. The permeability values were low and satisfied local criteria, namely 1.052 x 10-5 (V1), 1.260 x 10-5 (V2), and 1.394 x 10-5 (V3). Composite V2 has the best value with a cohesion value of 55 kPa and a shear angle of 22°.Therefore, composite V3 was the most promising composite as an alternative covering material for landfills because it has the lowest atterberg limits results, the lowest OMC, and the results from the direct shear test that meet the criteria even though it is not the best result among the three variations. However, by supporting the OMC value and good plasticity, the V3 composite was chosen. This study is beneficial and valuable in the engineering, selection, and design of materials used for the construction of landfill liners. .

The Yellow River Basin is an important economic zone in China and an important base for energy, chemical, raw materials and basic industries. However, the sloppy development pattern of "high consumption and high pollution" in nine provinces along the Yellow River Basin has become an important obstacle to achieving the carbon peaking and carbon neutrality goals. Using the extended STIRPAT model and combining scenario analysis and Monte Carlo simulation to build a prediction model, the future trend of carbon emissions in the nine provinces of the Yellow River Basin was predicted and the optimal path to reach the peak of carbons in the Yellow River Basin was explored. The results show that the nine provincial areas in the Yellow River Basin will not be able to reach the carbon peaking before 2035 under the baseline scenario. By further comparing the carbon peaking conditions of provinces under the low-carbon scenario and the technology breakthrough scenario, it is found that the peaking time, carbon peak value and the number of provinces under the technology breakthrough scenario are significantly better than those under the low-carbon scenario. Under the technology breakthrough scenario, the energy efficiency of the nine provinces in the Yellow River Basin will be greatly improved, the innovation capacity will be significantly enhanced, and the synergistic emission reduction mechanism and related policies between provincial areas will be perfected. Technological breakthroughs will become an important engine for high-quality development in the Yellow River Basin.