The heavy metal speciation analysis in sediments helps us understand and evaluate essential and unavoidable issues in terms of both health and environmental hazards imposed by these metals in our lives. Analyzing the total content of heavy metals enables us to understand only the quantity of the contaminants. To understand the different species or the chemical forms of heavy metals available in the sediments, we must study their speciation. Speciation studies help us determine their possible sources as well as their environmental stability in terms of availability to plants and other organisms. The heavy metals in this study were specified using four-stage sequential extraction, also known as the BCR technique. This study mainly highlights the quantification of metal contamination of Cu, Zn, Pb, Ni, Cd & Cr, and chemical forms as species in sediment samples collected from different Pune District, Maharashtra sites. Heavy metal contamination from the collected samples was analyzed with the use of flame atomic absorption spectrometry. This study indicated that Zn and Ni are among the most abundant metals in the sediment samples; however, Cu and Cd belong to the least abundant category. The oxidizable and residual forms (immobile and cannot be used by the organisms readily) appeared dominant for most heavy metals. Very significant differences were observed in the speciation of heavy metals from sample to sample, which was probably due to differences in water/soil composition and the agrochemicals like pesticides, weedicides, and fertilizers used in agricultural practices; the wastewater generated from different pharmaceuticals, chemical processing and manufacturing industries as well as the improper wastewater treatment methods.

In the context of promoting high-quality development in the Yellow River Basin (YRB) of China, urgent action is needed to achieve the “Dual Carbon” goal through energy savings, emission reductions, and industrial upgrading. This study measures carbon emissions from eight types of energy consumption across 43 industries from 2000 to 2019. Using the Kaya-LMDI model, factors affecting carbon emissions are analyzed, and the relationship between industrial structure and carbon emissions is explored through the coefficient of variation (CV). The findings reveal that coal consumption remains significantly higher than other energy sources, and the effect of energy structure adjustment on carbon emission reduction is limited compared to the impact of energy consumption increase on carbon emission growth. Moreover, the economic output effect is identified as the primary driving factor of carbon emissions, while energy utilization rate is crucial in achieving energy savings and emission reductions. Finally, the CV of carbon emissions across 43 industries is increasing. Based on these results, we suggest several policy recommendations, including prioritizing ecological concerns, developing comprehensive and scientifically sound plans, optimizing energy consumption structure, improving energy utilization efficiency, and adjusting industrial structure to promote sustainable development in the YRB.

To become the fastest-growing large economy in the world, India has set a target growth rate of 9%, reaching an economy of $5 trillion by 2024-25. It is an immense challenge to meet the growth target and keep the CO2 emissions under control. The present paper aims to discover the determinants for explaining CO2 emissions in India by conducting a complete decomposition analysis, where the residuals are fully distributed to the determinants for the country from 1990-2018. The analysis reveals that the biggest contributor to the rise in CO2 emissions in India is the expansion of the economy (scale effect). The intensity of CO2 and the change in the composition of the economy, which nearly move in tandem, also contribute to the rise in CO2 emissions, although more slowly. A declining energy intensity of the Indian economy is responsible for a considerable reduction in CO2 emissions. As a typical result for an upcoming economy, this paper did not find evidence for an environmental Kuznets curve. This implies that continued economic growth will lead to increased CO2 emissions.

Over the past decades, energy forecasting has attracted many researchers. The electrification of the modern world influences the necessity of electricity load, wind energy, and solar energy forecasting in power sectors. Energy demand increases with the increase in population. The energy has inherent characteristics like volatility and uncertainty. So, the design of accurate energy forecasting is a critical task. The electricity load, wind, and solar energy are important for maintaining the energy supply-demand equilibrium non-conventionally. Energy demand can be handled effectively using accurate load, wind, and solar energy forecasting. It helps to maintain a sustainable environment by meeting the energy requirements accurately. The limitation in the availability of sufficient data becomes a hindrance to achieving accurate energy forecasting. The transfer learning strategy supports overcoming the hindrance by transferring the knowledge from the models of similar domains where sufficient data is available for training. The present study focuses on the importance of energy forecasting, discusses the basics of transfer learning, and describes the significance of transfer learning in load forecasting, wind energy forecasting, and solar energy forecasting. It also explores the reviews of work done by various researchers in electricity load forecasting, wind energy forecasting, and solar energy forecasting. It explores how the researchers utilized the transfer learning concepts and overcame the limitations of designing accurate electricity load, wind energy, and solar energy forecasting models.

A strategic shift towards sustainable, appropriate supply chain networks and data-driven decision-making in solid waste management in rural and urban areas can drastically reduce environmental pollution. This study utilizes a hybrid strategy of genetic algorithms and fuzzy logic to improve the supply chain network in solid waste management in Lagos State. In this research, four local governments in Lagos State are taken as a case study to help Identify solid waste in those selected areas, acquire data to better understand the supply chain network in solid waste management, and use the data acquired to model for the algorithm. A series of 30 iterations were carried out using a fitness parameter of frequency, price range, and means of disposal to determine who should be given utmost importance in the chain. Supply chains often exhibit inadequacies that may be enriched using Artificial Intelligence (AI) tools. The optimization model is flexible and useful, so everyone involved in the chain can coexist harmoniously. One of the reasons causing these inadequacies in proper waste management is a poorly planned supply chain network. It was concluded that the scavengers must be recognized as major participants in the movement of waste from houses to these provided refuse bins, with their frequency increased to 6 times daily with dustbins ranging from 9-20 be provided on each street which the private service participants (PSP).

In this study, we summarized our own and literature data on the helminth fauna in amphibians, reptiles, birds, and mammals inhabiting the Mordovia Nature Reserve (European Russia). The history of research on parasitic worms in vertebrates has more than 70 years here. Nowadays, 242 species of helminths have been identified in vertebrates in this protected area: 54 cestodes, 87 trematodes, 98 nematodes, and 3 acanthocephalans. Of these, 169 helminth species have an indirect life cycle, while 73 develop directly. 217 revealed parasite species use vertebrates as definitive hosts and 21 as intermediate and/or parathenic hosts. Three species of trematodes, Gorgoderina vitelliloba, Haplometra cylindracea, and Opisthioglyphe ranae combine the larval and mature lifestyle stages in amphibians. The most diverse helminth fauna is in rodents (41 species), birds (38), artiodactyls (37), and insectivores (35). Less rich in amphibians (32), bats (32), reptiles (26), and carnivores (19). Only six parasite species are found in hares. Most of the helminth species recorded in the vertebrates of the Mordovia Nature Reserve belong to the Palearctic faunistic complex – 107 species. Fifty-eight species are cosmopolitan. The range of 39 species covers the Holarctic. The distribution of 37 species of helminths is limited to Europe. Seventy-three of 242 species found in the nature reserve’s vertebrates have medical and veterinary importance as potential pathogens of dangerous zoonoses.

The quality of surface water has a significant impact on human health and the entire ecological system. Sewer spillages from the surrounding informal settlements discharging into the river, carrying high concentrations of fecal coliforms, are one of the major causes of extreme pollution in the rivers of South Africa. These informal settlements are common in many developing countries, and they are usually located near waterways to compensate for basic demands for water, sanitation, and recreational space, where municipal infrastructure lags behind urban growth. One major problem has been poor sanitation and poor waste disposal practices in the informal settlements, which has led to the contamination of water resources. This study aims to assess the extent to which poor sanitation in informal settlements impacts the water quality of South African rivers, given the rapid rise in population and unemployment rate. The study also highlights health and environmental issues in the local regions caused by poor sanitation. Contamination of water bodies is associated with serious health problems and fatalities. Therefore, there is a need for frequent monitoring and management of waste products discharged into the neighboring aquatic environments.

Soil is one of the largest carbon reservoirs sequestering more carbon than vegetation and atmosphere. Due to the enormous potential of soil to sequester atmospheric CO2, it becomes a feasible option to alleviate the current and impending effects of changing climate. Soil is a vulnerable resource globally because it is highly susceptible to global environmental problems such as land degradation, biodiversity loss, and climate change. Therefore, protecting and monitoring worldwide soil carbon pools is a complicated challenge. Soil organic carbon (SOC) is a vital factor affecting soil health since it is a major component of SOM and contributes to food production. This review attempts to summarize the information on carbon sequestration, storage, and carbon pools in the major terrestrial ecosystems and underpin soil carbon responses under climate change and mitigation strategies. Topography, pedogenic, and climatic factors mainly affect carbon input and stabilization. Humid conditions and low temperature favor high soil organic carbon content. Whereas warmer and drier regions have low SOC stocks. Tropical peatlands and mangrove ecosystems have the highest SOC stock. The soil of drylands stores 95% of the global Soil Inorganic Carbon (SIC) stock. Grasslands include rangelands, shrublands, pasturelands, and croplands. They hold about 1/5th of the world’s total soil carbon stocks.

In this study, waste tires were used to develop activated carbon for the adsorption of Cr(VI) from aqueous solutions, and an artificial neural network (ANN) model was applied to predict the adsorption efficiency of waste-tire activated carbon (WTAC). SEM and FTIR were used to characterize the developed WTAC. A three-layer ANN with different training algorithms and hidden layers with different numbers of neurons was developed using 79 data sets gathered from batch adsorption experiments with different initial Cr(VI) ion concentrations, contact periods, temperatures, and doses. Conjugate gradient backpropagation of Powell-Beale restarts (traincgb) was found to be the best training algorithm among all the training algorithms, with an RMSE of 5.894 and an R2 of 0.985. The ANN topology had 4, 8, and 4 neurons in the input, hidden, and output layers. The correlation coefficient of the ANN models of Cr(VI) ion adsorption efficiency is 0.977.

The River Chief System is an administrative model of water environment governance currently adopted in China. Under this system, the chief CPC and government leaders at various levels serve as “river chiefs” and are responsible for organizing and directing the management and protection of the rivers and lakes within their remit. This paper tries to reveal the actual effectiveness of the River Chief System based on the behaviors of grassroots river chiefs (GRCs). First-hand data about GRCs is obtained through a questionnaire survey. Whether the water environment governance target is achieved and the water quality change of the river sections in the charge of GRCs is quantitatively assessed It has been found that, except for implementing “one policy for one river” and making river patrols, the behaviors of GRCs have no positive effect on river pollution prevention and control, implying the ineffectiveness of the River Chief System. The framework design of the River Chief System should be optimized, and a system with professionals to support GRCs in performing their duties should be established. Moreover, the tendency to use environmental regulation as a mandatory policy tool should be weakened. These measures are of great practical significance to the implementation of the green development concept and the furthering of the River Chief System overall.