Sustainable clinical trials involve conducting trials in a socially conscious and environmentally responsible manner. This involves considering the effects of clinical trials on the environment and the populations engaged in the studies. The pharmaceutical sector, particularly clinical research, is a large contributor to greenhouse gas emissions. The need for a legal framework considering the environmental impact of hundreds of global clinical trials cannot be overstated. Clinical trials’ carbon footprint is a complex subject that calls for cooperation from various parties, including researchers, trial sponsors, healthcare providers, and regulatory organizations. The waste generated during clinical trials, including packaging materials, laboratory supplies, and hazardous waste from the disposal of clinical samples, can adversely affect public health and the environment. Therefore, addressing this issue is essential to ensure that clinical trials are conducted in an environmentally and socially responsible manner. The purpose of this study is to discuss potential strategies to cut down on carbon emissions, discuss the challenges in setting up clinical trials in an environmentally sustainable way, and highlight the importance of a precautionary approach during the various phases of conducting clinical trials. Although there is limited research on greenhouse gas emissions generated by clinical trials, it is evident that more work needs to be done in this field.

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.

The expanding nuclear industry has led to increasing radioactive waste in the environment. Exposure to these wastes causes considerable irreversible damage to the organisms, some of them being even lethal. Conventional methods like incineration, wet oxidation, and acid digestion have been used for radwaste treatment to control this. Apart from them, other organic methods like bioremediation are being widely applied by scientists. Many bacteria, fungi, algae, and plants are observed to possess remediating properties. Hence, these are now used on a large scale to treat the radioactive matter as quickly and effectively as possible. Techniques like bioaccumulation, enzymatic reduction, bioprecipitation, or phytoremediation methods such as phytoextraction and phytostabilization involving such organisms with remedial abilities have successfully removed the radioactive matter to an extent from the contaminated site. Further research is needed to increase the efficiency of the techniques and help remove radionuclides in an environment-friendly manner.

Environmental issues have continued to spur discussions, debates, public outrages, and awareness campaigns, inciting interest in emerging technologies such as Artificial Intelligence. Its usage is spread across many environmental industries, including wildlife protection, natural resource conservation, clean energy, agriculture, energy management, pollution control, and waste management. In 2017, at the United Nations Artificial Intelligence Summit in Geneva, the UN acknowledged that AI could be an enabler in the sustainable development process towards peace, prosperity, and dignified life for humankind and proposed to refocus on the application of AI in assisting global efforts on sustainable development to eradicate poverty, hunger and to protect the environment as well as to conserve natural resources. It is vital to address environmental sustainability concerns; however, with the advent of AI, most common environmental issues are now solvable by prioritizing human interests. Sustainability encompasses the interrelated areas of the environment, society, and economy. According to the United Nations’ “Our Common Future,” also known as the “Brundtland Report,” it is defined as “development that satisfies current needs without compromising the ability of future generations to meet their own needs.” Unfortunately, the Earth is currently facing serious consequences from global warming and climate change, and immediate action is required to encourage the use of environmentally friendly and sustainable products to address these issues. Environmental degradation and climate change are numerous environmental concerns requiring novel and intelligent artificial intelligence solutions. The literature on AI and environmental sustainability encompasses various domains. Notably, AI is being used to address the bulk of regional and global environmental concerns, including energy, water, biodiversity, and transportation, even though many of these sectors have permeated and evolved. However, there is a need to combine current literature on the application of AI, particularly in relation to environmental sustainability in areas such as energy, water, biodiversity, and transportation. There is a significant lack of research on how AI can promote environmental sustainability. This research aims to explore how AI can be applied to address environmental issues in various sectors to achieve the Sustainable Development Goals (SDGs).

There is now a worldwide collective obligation, a reality, to acknowledge environmental challenges. The paper discusses and analyses the principles of international environmental laws and how those are applied in international conventions and treaties, and the effectiveness and weaknesses of those laws. This discussion mainly focuses on the principles’ backdrop, what they mean, and how they have been adopted in international environmental law. Furthermore, the paper focuses on the outcomes of formulating the principles and enforcement of the legal framework. It analyses their prospect to strengthen the legal framework to achieve the objective of these principles. Besides, some recommendations have been made to strengthen these legal frameworks. It shows why it is essential to form efficient environmental platforms in present climate issues and how all nations can be brought under a common platform where they can take decisions regarding the safeguard amid the evolving environmental situations.

Levrier Bay, located in the western part of Mauritanian Cost, has a strategic position in Mauritania’s fish economy and reproduction environment. Recently, fishmeal factories have multiplied in the bay. This study was carried out in Levrier Bay. It is the first one in this area which is interested in assessing the environmental impact of fishmeal factory discharges by measuring several parameters such as suspended matter, chemical oxygen demand (COD), biological oxygen demand (BOD), conductivity, turbidity, and salinity. A total of 27 samples were collected at 9 sites distributed on the link between effluents from factories and the Atlantic Ocean (discharge site). Results show that some parameters are over permissible values, like suspended matter content (SS), which reached 2020 mg. L.-1 level. The turbidity measure shows excessively high values (50 to 961 mg.L.-1); impacted by effluents at the reject point, the seawater conductivity and salinity are particularly low (4.53 to 188.2 and 13 to 56.4, respectively). The total organic carbon (TOC) values ranged from 200 to 780 mg/L, whereas the highest measured level of chemical oxygen demand was 4010 mg.L.-1 Biochemical oxygen demand content ranged from 685 to 961 mg/L. The biodegradability index (COD/BOD) shows that these effluents are not easily biodegradable because the index > 3.

Drought assessment using drought indices has been widely carried out for drought monitoring. Remote sensing-based indices use remotely sensed data to map drought conditions in a particular area or region. Therefore, the objective of the present study is to make a study on drought risk based on the calculation of an indicator from biophysical parameters extracted from NOAA/AVHRR satellite data, namely TCI and VCI, to obtain a better understanding of the differentiation between each index, and their application for drought monitoring in the High Atlas of Marrakech on the Chchaoua Morocco watershed during 1980-2020. Landsat oli7 and8 data were used to construct the indices. The result showed that each index proved to be a useful, fast, sufficient, and inexpensive tool for drought monitoring. However, each index has its differences. The TCI was found to be drought sensitive during the dry season or in months when high temperatures occurred. While VCI detected drought more sensitively in the rainy season as well (December-January-February to May) than TCI and VCI. Meanwhile, VCI, including the improved TCI, combined two indicators to better understand drought occurrence. These indices were calculated using GIS, QGis, ArcGis satellite imagery scenes, and Landsat. After a comparative study of these years, from 1984 to 2020, the evolution of the VCI and TCI was highlighted.